dataesr/scientific-paragraphs-categorization huggingface dataset:
With the same comparison parameters (two mismatches and end gaps/insertions), we found 14 out of 552 (2. 5%) spacers intersecting between CRISPR-A and CRISPR-D repeats in fully sequenced genomes. This fraction closely matches that obtained in the analysis of the spacers in the Japanese Beppu dataset, and further suggests that cross-amplification of spacers associated with CRISPR-A and CRISPR-D repeats is negligible. Amplification of SPV-borne mini-CRISPR arrays. Three pairs of primers were designed to amplify the SPV-borne mini-CRSPR arrays and their leader sequences (Supplementary Fig. 5 ; Supplementary Table  3 ). All primer pairs match perfectly to both SPV1 and SPV2 genomes. Amplification with the three pairs of primers using DNA isolated from SPV1 virions, J15 (20 days) and J14 (20 days) enrichment cultures yielded PCR products with expected sizes (Supplementary Fig.
5 ). The sequences of all nine amplified products were confirmed by Sanger sequencing. Spacer extraction and clustering. Spacer sequences were extracted using spget program (https://github. com/zzaheridor/spget). Spget identifies degenerate sequences of CRISPR repeats and PCR primers, and extracts all sequences between them. To account for possible sequencing mistakes and natural CRISPR repeat diversity, additional 2-5 mismatches were allowed in repeat and primer sequences. Based on expected spacer lengths, extracted spacers shorter than 25 nt or longer than 60 nt were filtered out. An additional quality filter was applied-only spacers with all nucleotides sequenced with the Phred score value higher than 20 were used for further analysis. The described filtering resulted in the removal of ~25% of all spacers.
The clustering was performed by UCLUST program  61  , with 85% identity threshold and zero penalties for end gaps. UCLUST algorithm was also used with 85% identity threshold to find common spacers for different sets. The coverage of spacer diversity was estimated with Good's criterion: C = 1 -(N/total number of clusters), where N is the number of sequences that occurred only once or twice. The alpha diversity (Shannon entropy) and Chao estimate of coverage were calculated using the R package vegan (https://cran. r-project. org/web/packages/vegan/index. html). The spacer sequences are available in Supplementary Data 1. Reconstruction of CRISPR arrays. The procedure of CRISPR array reconstruction uses pairs of neighboring spacers obtained from NGS reads. All pairs for the sample are joined into a directed graph, where each node represents a spacer, edges connect the spacers that appeared together in a pair, and the number of found pairs in NGS reads is used as an edge weight.
8. Several arrays share the same terminal, leader-distant spacer, some of the arrays are branching towards the leader end. The script for reconstruction of the CRISPR array graphs is implemented in R language (https://www. R-project. org/)  62. Because this approach is not suitable for identification of mini-CRISPR arrays, we used the eccentricity metrics (the length of the longest path, which is going through the selected node). The eccentricity number shows the length of the longest CRISPR array, which can be reconstructed using selected spacer (see Supplementary Fig. 7B ). Each Sulfolobales genome usually contains more than one CRISPR array with the same CRISPR repeat sequences. We observed groups of spacers from three independent graph components with linearly correlated frequencies in two samples (Supplementary Fig.
9 ), which is consistent with them being sequenced from the same genome. Protospacer analysis. Protospacers were searched for with BLASTN  63  (word size 8, e-value < 0. 01) in local databases of Sulfolobales viruses, plasmids and cellular genomes. PAMs were identified by aligning flanking sequences of protospacers. For Fig. 2A , chi-square test followed by Bonferroni correction was used to test the specificity of spacers associated with different CRISPR consensuses to different sources of protospacers (Sulfolobales host genomes, viruses or plasmids) based on total number of spacers for each CRISPR consensus and total number of hits to a particular source. To calculate the P-value for the coincidence of spacer targeting and regions of divergence in SPV1 and SPV2 genomes, we aligned SPV1 and SPV2 genomes with ClustalW  64  and manually verified the quality of nucleotide sequence alignment.
Next, we calculated the number of spacers in our dataset that match fully conserved regions of the SPV1-SPV2 alignment (22 spacers) and variable parts of the alignment (42 spacers). We simulated 10,000 spacers from random positions of SPV1-SPV2 alignment and calculated the number of simulated spacers from conserved regions (5674 spacers) and variable regions of the alignment (4326 spacers). Chi square test was performed for observed (22 and 42) and expected (5674 and 4326) values; the resulting P-value was 0. 006. Loss of minor spacers during cultivation. The error bars indicate the confidence interval for the proportion of lost spacers calculated as conf = z(0. 975)*sqrt(lost* (1-lost)/N), where 'lost' is a fraction of lost spacers and N is the total number of spacers in each group. Assembly of viral contigs from spacers.
To reconstruct the viral contigs, we performed "all spacers against all spacers" BLASTN (word size 8, identity > 0. 7). Then a graph of spacers was built, where spacers are connected if they were matched by BLAST search. The graph was decomposed, spacers from the largest subgraphs were aligned with MUSCLE  65  , and the alignment was manually corrected. Prediction of mini-CRISPR arrays in the CRISPRome data. Mini-CRISPR arrays in the CRISPRome data were predicted based on the abundance of spacers. This section explains the abundance thresholds selected to distinguish spacers derived from long arrays and mini-arrays. By definition, mini-CRISPR arrays contain only 1 or 2 spacers. The procedure of CRISPR array reconstruction from the CRIS-PRome data resulted in multiple mini-CRISPR array candidates with only 1 or 2 spacers.
Acknowledgements This work was supported by l' Agence Nationale de la Recherche  (project  ENVIRA , # ANR-17-CE15-0005-01 ) and the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  685778  (project  VIRUS-X ). E. V. K. is supported through the  intramural program  of the U. S. National Institutes of Health. S. M. was supported by  Vernadski  fellowship from  Campus France ,  RSF 19-14-00323  and  NIH  grant  GM10407  to K. S.
Confocal fluorescence imaging and image analysis. Confocal experiments were performed on a Nikon Eclipse TE2000 inverted microscope. The software EZ-C1 was used to make the acquisition of the confocal images. The imageJ radial profile plugin was used to analyze the fluorescence signal on the GUVs. Protein density was measured as described in supplementary material of Aimon et al. 39  Briefly, the fluorescence signal of a reference fluorescent lipid (Oregon-Green® 488 DHPE) was measured on GUVs at known densities. The fluorescence of GFP-septins and Oregon-Green was subsequently compared in solution. Knowing the geometrical parameters of septin filaments and the relative fluorescence intensity of GFP and the reference fluorophore, we were able to deduce the density of septins bound to a GUV from the intensity of fluorescence signal.
Cryo-tomography. The samples were prepared as described above. 10 nm size gold beads were added to the solution before being plunge-frozen. Tilt series were collected in low dose mode, every two degrees, using a Tecnai G2 (FEI, Eindhoven, Netherlands) microscope operated at 200 kV and equipped with a 4kX4k CMOS camera (F416, TVIPS). Images were collected following this angular scheme: 0 to -34°, then +2 to 60°and finally -36 to -60°to minimize irradiation at the lowest angles. The dose per image was 0. 8 electrons per Å 2. The imaging was performed at a magnification of 50,000 and each image was binned twice for a final pixel size of 4. 26 Å. The consecutive images were aligned using the IMOD software suite  40. Back projection was performed using IMOD and SIRT reconstruction was carried out using Tomo3d  41.
Noting θ the angle between those two vectors, the orientation persistence along the filament can be described as: hcos θi ¼ e dÀ1 ð Þ L 2Lp ð25Þ where the brackets denote the average over all starting position 0, d is the dimension (here d = 2) and L p is the persistence length of the filament. The filaments were tracked using the JFilament plug-in of imageJ (https:// imagej. net/JFilament). Supplementary Fig. 6A  shows a raw image of a septin filament and the same filament tracked by the software. This gives 2D coordinates for all points along the tracked filaments. With a matlab script, we extracted from these coordinates the tangent vectors and calculated 〈cosθ〉 for each value of L. We then plotted 〈cosθ〉 against L in a semi-log scale and extracted the persistence length value from the slope.
Acknowledgements This work was supported by  Institut Curie ,  Centre National de la Recherche Scientifique (CNRS) , the  Agence Nationale pour la Recherche  (grant  ANR-13-JSV8-0002-01  to  A. Bertin ). We thank the Cell and  Tissue Imaging  ( PICT-IBiSA ),  Institut Curie , member of the  French National Research Infrastructure France-BioImaging  ( ANR10-INBS-04 ). We acknowledge  Gérard Péhau-Arnaudet  at the  Imagopole (Institut Pasteur)  for some cryo-EM imaging. We thank  Virginie Bazin  and  Mickaël Trichet  from the  Electron Microscopy facility of the Institut de Biologie Paris-Seine (IBPS, Paris)  for some of the SEM sample preparation and technical assistance during acquisitions. We thank  Manos Mavrakis  and  Gijsje Koenderink  for sharing the TIRF data on passivated surfaces. Alexandre Beber  was funded by  Ecole doctorale PIF, Physique Ile de France.
Study Design, Study Population And Sampling Procedure The study was conducted in Parakou, a city in northern Benin (West Africa), specifically in the Borgou Department. This was a community-based cross-sectional study of women in the postpartum period. Inclusion criteria included all women living in Parakou who had given birth in the twelve months prior to the study. The data were collected from May 1-31, 2018. Women who had a tubal ligation or a hysterectomy, as well as those who did not consent to participate, were excluded from the study. Parakou consists of 58 villages and districts. 9 Sampling was conducted in two stages. For the first stage, half (29) were selected by simple random sampling. In the second stage, we selected households with women who were eligible for the survey. In the center of each selected district, we used the direction indicated by the throwing of a ballpoint pen, and then proceeded to number all the households in the chosen direction and select one household to survey in that area by simple random draw.
Techniques, Data Collection And Variables Data were collected via a pre-tested questionnaire. The dependent variable was defined as the use of at least one contraceptive method during the postpartum period. This was a binary qualitative variable measured from the woman's statement. This variable was coded « yes » if the woman reported using a contraceptive method at the time of the survey and « No » if otherwise. In order to identify factors associated with PF in the postpartum period, we included sixteen (16) independent variables at the level of the respondents. These included i) socio demographic features such as age, level of education, occupation, religion, marital status, husband's education level, socio-economic level, ii) factors related to the woman's reproductive history such as gravidity, parity, menses resumption, knowledge of contraceptives methods, contraceptive use history, resumption of sexual activities postpregnancy, last intended pregnancy, counseling during pregnancy/in the delivery room/in postpartum period, and iii) discussion around contraceptive use with husband/partner, need of husband's permission before adopting a contraceptive method.
Materials and methods 2. 1. Animals: Data were obtained from the INRA-UMR PEGASE experimental dairy station at Mejusseaume, Le Rheu, in western France (48°11'N; 1°71'W; elevation 35 m). Data were collected from May-June 2017. The study involved 30 dairy cows, with a mean (± 1 standard deviation (SD)) age of 3. 0 (± 1. 2) years and milk production of 25. 5 (± 3. 6) kg milk per day. After each milking (twice a day), cows were individually and automatically weighed on a static weighing station (DeLaval France, Elancourt, France) at the milking parlor's exit. Their mean (± 1 SD) weight was 674 (± 66) kg. Mean BCS (± 1 SD), based on the French scoring scale of 0-5  (Bazin et al. , 1984) , was 2. 1 (± 1. 2). Data from the 30 cows were used to estimate the accuracy of the measurements extracted from images from the new scanner and calculate correlations between these estimates and manual measurements of the cows.
This resulted in a single point cloud representation of the entire cow (Fig. 3 ). Two camera filters were used during image capture. The first was a physical filter centered on the laser wavelength to reduce ambient light and increase the contrast of laser stripes in each image. The second was a software filter. For this filter, since each camera-laser pair was fixed at a specific height on the structure, the distance between the ground and the camera was known. The depth of field of each camera varied from 0. 3-3. 5 m. A maximum depth was determined for each individual camera to prevent recording undesirable points that were too far from the camera and did not belong to the cows, such as walls. Other undesired objects (e. g. a cable) were easily deleted (Fig. 4 ) during a cleaning process, using Metrux2α® software (3DOuest company, Lannion, France).
This step ensures that the point cloud is a sampling of a smooth surface on which surface normal vectors can be estimated. An example of using Morpho3D with a live cow is available online (https://vimeo. com/219370900). Finally, surface normals were estimated from the point cloud, and a screened Poisson surface reconstruction algorithm was applied to build a triangulated mesh  (Kazhdan and Hoppe, 2013)  using Meshlab open-source software  (Cignoni et al. , 2008). Image-processing steps are shown in Fig. 4.
g. ischial width). Measurements were then taken of experimental cows. A plastic model cow was used to estimate repeatability (see next section). For manual measurements, we used permanent ink to identify measurement locations on the plastic model. For measurements extracted from 3D images, we first fixed small balls on the plastic model to precisely locate where to perform measurements. We then used a 3D image without the balls to extract reference measurements of the model. Six measurements were taken of live animals and extracted from images: heart girth (HG), chest depth (CD), wither height (WH), hip width (HW), backside width (BaW) and ischial width (IW) (Fig. 6 ). For live animals, a tape measure was used to measure HG, while a height gauge was used for the other measurements. For images, the relevant measuring location was determined visually, and measurements were extracted using Metrux2α® software.
A major concern, currently under review, is to automate the steps of image analysis: cleaning, reconstruction and extraction of measurements. This is clearly a major point when using this method at a large scale and when developing new technology based on "one-shot" technology, in which animal movement is no longer a problem. In the latter case, image definition will probably decrease, but based on developments in the Morpho3D project, accurate equations and algorithms should be available to optimize analysis. The ability to capture a 3D image of an entire animal will allow for many potential improvements in the near future. Automatic estimation of visual BCS, morphological scoring for selection purposes, BW, surface area and volume will be developed and implemented in Metrux2α® software and compared to manual measurements.
Acknowledgments The authors wish to thank everyone involved in the  Morpho3D  project, especially technicians at the  Mejusseaume experimental station , who took excellent care of the animals. The  Morpho3D  project is supported by the  National Fund CASDAR , which supports innovation in agriculture (RFP " Recherche Technologique " 2015, no. 005 ), special funds from the  INRA Animal Physiology  and  Livestock Systems division for innovative projects and the collaborative ANR -APIS-GENE  project  DEFFILAIT.
Linkage map construction with LepMAP The identity by descent (IBD) of all individuals was first checked using plink1. 9 to confirm family identities and detect individuals with abnormal genotypes  (Purcell et al. 2007; Chang et al. 2015). The genetic linkage map was built using a combination of modules from LepMAP2  (Rastas et al. 2016 ), LepMAP3  (Rastas 2018 ) and custom Perl scripts (see supplementary methods section (a) for more detail). Marker names give the Hmel2 genome scaffold followed by scaffold position. Before linkage map construction, one individual (PS360) was removed from mapping family B14 due to very high levels of missing data, and three individuals (PS252, PS699, and PS703) were removed from B10 after showing a lower IBD than expected (indicating family misassignment). Quality filtering gave a final set of ~150,000 good quality genotype markers from which to build a linkage map.
Quantitative hindwing red variation All individuals in families B14 and B10 with rays on the ventral side of the hindwing were used to analyse quantitative ventral hindwing red variation. Fifteen landmarks (see Fig. S2 ) were manually placed at wing vein intersections in tpsDig2  (Rohlf 2013 ) and quantitative ventral hindwing red variation was then analysed using functions from the R package patternize  (Van Belleghem et al. 2017 ). patLanRGB(), which uses landmark registration, was used to align the rays on each wing. Defining red as R226, G11, B26 (cutoff = 0. 40) was found to accurately distinguish red and orange pigmentation from brown/ black pigmentation. plotHeat() was then used to plot a heat map showing the proportion of individuals with 'red' at each pixel. Principal component analysis (PCA) was carried out with patPCA().
Medial band shape The seven distinct elements of the medial forewing band were sampled using equally spaced semi-landmarks around the margins of each element, using the TPS software  (Rohlf 2013). In order to maintain a consistent number of semilandmarks, in rare cases where one of these elements was missing, the points around this missing element were overlaid at the location of the missing element. Partial Generalised Procrustes Superimposition was then carried out using the gpagen command from the R package geomorph  (Adams and Otárola-Castillo 2013) , this slid the position of each point along the curve so that it was optimised based on Procrustes distance. In order to show the segregation of band shape from each colour pattern race in the progeny, PCA was carried out using prcomp(), with 10 randomly chosen stock individuals from each race.
Mendelian fine interval mapping To fine map hindwing rays and broken band we used individual genotypes to identify zero-recombinant intervals. While the rays phenotype was unambiguously present or absent, we used the cell spot phenotype to determine the absence or presence of the broken band, as the Belém spot shows significantly variable penetrance. We used the same SNP dataset as used for linkage mapping, but reduced it to just the scaffolds containing optix (for rays) and WntA (for broken band) and removed SNPs with a minor allele frequency less than 10% in each of our two broods. As both presence of rays, and absence of the broken band are dominant, to isolate zero-recombinant intervals associated with our traits we identified SNPs at which all individuals with the recessive phenotype were homozygous for one allele, while all individuals with the dominant phenotype were either heterozygous or homozygous for the alternative allele.
1). In our dataset we had only two markers within this ray region; at 768,257 and 768,274 bp. These markers did not show a diagnostic pattern in our analysis, and so our analysis slightly narrows this ray module. The next closest marker in our dataset was (at 842,688 bp) upstream of this ray region, and was found to be our first diagnostic marker. Our results therefore corroborate those from  Wallbank et al. (2016)  indicating the ray module is upstream of optix and somewhere upstream of position 768,274 bp on this scaffold. The results of Wallbank et al. (  2016 ) were based on Genotype × Phenotype associations using de novo assembled genomes from wildcaught specimens, and so our confirmation from QTL crosses is valuable.
Data archiving Unprocessed RAD reads are available on the EBI ENA short read archive (Study accession PRJEB25234). Genotypic and phenotypic data for QTL analyses (formatted for R-qtl), as well as morphometric data (TPS format), along with photos of all individuals from the analysed B10 and B14 families are available from Figshare (https://doi. org/10. 6084/m9. figshare. 5928556). Supplementary information is available at Heredity's website.
Acknowledgements This research was funded by the  Natural Environment Research Council  (grant  NE/K012886/1 ) to  Kanchon Dasmahapatra. Jake Morris  was supported by a  NERC  studentship and a  Heredity fieldwork grant  from the  Genetics Society. James Mallet  was supported by start-up funds from  Harvard University. We would like to thank the governments of Peru and Suriname for permission to collect butterflies, and the  York Biosciences Technology Facility and FAS Center for Systems Biology  where library preparation and sequencing were carried out respectively. We would also like to thank the  Horticulture Team  ( Department of Biology, University of York ), and in particular  Paul Scott  and  Chris Lancaster , for help growing plants and maintaining butterfly stocks. We thank  Ewout Eriks  from the  Neotropical Butterfly Park Suriname  for help collecting wild specimens, organising collection and export permits as well as access to his facilities.
Trial design We conducted a non-inferiority, randomized, doubleblind, controlled trial in infants hospitalized for viral bronchiolitis in the pediatric department of the Grenoble Alpes University Hospital. Children were screened from the pediatric ED during the epidemic seasons of bronchiolitis in 2014 and 2015 and included at the end of the first 24 h following admission. Follow-up was maintained until discharge from hospital. Background, demographic information and medical data were collected through standardized case record forms. Study endpoints were based on data collected at clinical examinations performed at 24, 48 and 72 h of treatment and at the time of discharge. There was no change in routine bronchiolitis monitoring or supplementary exams. Viral research was not routinely performed.
Statistics Qualitative variables were noted as numbers and percentages, and quantitative variables as means and standard deviation. Baseline patient demographic and clinical characteristics between intervention and control groups were compared with Student test for quantitative variables, and χ 2 or Fisher exact test for qualitative variables. The Wang CSS difference described by mean and 90% confidence interval was evaluated in a per-protocol analysis while secondary outcomes were analyzed in relation to intention-to-treat. The evolution of the average CSS score between the 2 arms was compared using an analysis of variance for repeated measures. P-values less than 0. 05 were considered statistically significant and analyses were performed with Stata software version 13 (Stata Corp 4905 Lakeway Drive College Station, TX 77845 USA).