dataesr/scientific-paragraphs-categorization huggingface dataset:
5 3 2 lm were counted in each specimen (N 5 42) and the diameter of each fibril present in the selected area was measured. To characterize diameter distribution, all fibril diameters measured were plotted per species, within class intervals of 30 nm of the collagen fibril diameter. Based on the density and diameters of the collagen fibrils, the total area of collagen in a surface of comparable size was calculated using the Image Tool 3. 0 software. In addition, the CSA of each collagen fibril was obtained by transforming the diameter data into radius, which was squared and multiplied by pi (Appendix 2 in supporting information). To characterize tendon ultrastructural morphology, the following statistic descriptors were obtained: abundance, estimated as the number of fibrils belonging to a size range per species.
| Statistical analyses The variables were Log 10 -transformed to meet requirements of normality  (Zar, 1999). First, we calculated a correlation matrix (Pearson correlations) to see whether variables were correlated. Next, we tested for differences in snout-vent length between locomotor modes using an ANOVA. Given that no differences in snout-vent length were detected we ran a MANOVA on the fibrillar and tendon CSA data to test for differences between species with different locomotor modes. To explore whether and how phylogeny may impact our data we ran a factor analysis with varimax rotation on the Log 10 -transformed data of snout-vent length, fibrillar CSA, and tendon CSA and plotted the phylogeny in the morphospace using the 'phylomorphospace' function in R (R Core Team, 2016) implemented in the 'phytools' library  (Revell, 2012).
ACKNOWLEDGMENTS The authors are deeply indebted to  Beatriz Winik ,  Carolina Santolalla  and  Manuel Siñeriz  of the  CIME, Universidad Nacional de Tucum an , and to  Alicia Castro ,  Jorge Mukdsi , and  Patricia Pons  of  Universidad Nacional de Cordoba  for their technical assistance. The critical reviews of Daniel Dos Santos (IBN;  Universidad Nacional de Tucum an ),  David Parry  ( Massey University, New Zealand ),  Nikolai Konow  ( UMass Lowell ) and one anonimous reviewer are very much appreciated. Grant sponsor:  CONICET ,  PIP 0389 ;  Marie-Skłodowska Curie fellowship ,  EU  project  655694 -GETAGRIP; FONCyT PICT 2015-1618 ;  FONCyT PICT 2016-2772.
Materials and methods We retrospectively reviewed all adult patients more than 18 years old who underwent surgical removal of a complicated MD at European Georges Pompidou University AP HP hospital in Paris and Avicenne University APHP hospital in Bobigny, which are tertiary French medical centers. We obtained a favorable ethical approval to conduct this study and to publish the results at the first session of 2018 (01012018). The databases at the Department of Pathology, European Georges Pompidou University APHP hospital in PAris, France and at Avicenne University APHP hospital in Bobigny, France were screened for surgical specimens of MD obtained between January 2001 and June 2017. The following data were collected by retrospective medical record review: demographic variables, preoperative diagnosis, diagnostic method, and pathology data.
Library preparation. Primers amplifying toll-like receptors TLR1, TLR4, TLR5, TLR6, TLR7 and TLR9 were designed in conserved regions of aligned rodent mRNA sequences obtained from the NCBI GenBank. Despite several attempts, we were not able to design primers amplifying TLR3 and TLR8. TLR2 was amplified using two pairs of overlapping primers described in  13. To minimize the PCR error rate, we used high fidelity Phusion polymerase (Thermo Scientific). The PCR mix contained: 5x polymerase buffer, 200 uM of each dNTP, 0. 5 uM of each primer, 0. 3 U of polymerase, and 10-50 ng of genomic DNA. Details of both primers' melting temperature and annealing time are given in Table  S3. Amplified fragments were pooled for each individual in similar quantities based on intensities of resolved bands by agarose gel electrophoresis  24  , and purified twice using CleanUp kit (Aabiot).
Assembly and genotyping. To construct a reference for read mapping, an unannotated bank vole genome assembly was retrieved from the NCBI GenBank (PRJNA290429). Mouse transcripts (accession numbers NM_001276445, NM_011905, NM_021297, NM_016928, NM_011604, NM_001290755, NM_031178) of the studied TLR genes were mapped against the bank vole genome using the -f samse option of gmap tool (http:// research-pub. gene. com/gmap)  25. The regions against which mouse transcripts mapped were extracted with ~1000 bp offset using getfasta function in Bedtools ver. 2. 25 (http://bedtools. readthedocs. io/en/latest)  26 and used as reference in the downstream analysis. The TLR9 locus in the bank vole genome contained two unassembled parts (-NNN-). Thus, after initial mapping as described below, we designed internal primers (TLR9_intF 5′TCTGCCCAACCTCCATACTC3′ and TLR9_intR 5′ TCTTATGGTCAGGGGTGCTC3′), which, together with the TLR9 F/R primers listed in Table  S3 , were used to amplify regions overlapping the -NNN-parts.
The amplicons were Sanger-sequenced providing reference sequences with filled -NNN-parts that were used to construct a gap-free reference sequence for this locus. Paired-end reads were mapped against the reference using bwa-mem ver. 0. 5. 9 (http://bio-bwa. sourceforge. net) with default parameters  27. The resulting bam files were merged, and deduplicated using the Picard MarkDuplicates tool (http://broadinstitute. github. io/picard). Duplicated read-pairs constituted 87% of the reads. For the further analysis, we retained only properly mapped and paired read pairs (both reads mapped, correct insert size) with a mapping quality ≥20, which resulted in 32 213 487 reads. The high coverage obtained (~100 000×) assured high confidence of the genotyping results. Variants were called in two-step procedure using the FreeBayes v0.
9. 10-3 (https://github. com/ekg/freebayes)  28. First, we ran FreeBayes with the following parameters: minimal fraction of alternate allele of 20% (-F 0. 2) as recommended  29  , minimum number of reads supporting alternate allele >2 (-min-alternate-count 2), and minimal read coverage >5 (-min-coverage 5). The results were filtered using vcffilter v. 41 (https://github. com/ vcflib/vcflib) specifying: QUAL/AO > 10 & DP > 10 & SAF > 0 & SAR > 0 & RPR > 0 & RPL > 0. In a second step, filtered high confidence variants were used to construct haplotypes using-hapolotype-basis-alleles function and specifying-max-complex-gap 37 (i. e. half of the read length). SNPs which were not phased by Freebayes were computationally phased using PHASE (http://stephenslab. uchicago. edu/software. html#phase)  30  and resolved into haplotypes using a custom Python script (included in the Supplementary Materials).
The reading frame in each locus was identified based on alignment with mouse transcripts, which allowed each SNP to be classified as synonymous or non-synonymous. Additionally, in each locus we identified leucine-rich repeats (LRRs) using the LRRfinder tool (http://www. lrrfinder. com)  31. Sequence variation and signatures of selection. Basic measures of sequence diversity -the number of variable sites (S), haplotype diversity (Hd), average number of nucleotide differences (k), and nucleotide diversity per site (pi) -were summarized in DnaSP v. 5 (http://www. ub. edu/dnasp)  32. Observed and expected heterozygosity and F is (inbreeding coefficient) were calculated using the R package diveRsity  33  , and the F st statistic was computed in Arlequin ver 3. 1 (http://cmpg. unibe. ch/software/arlequin35)  34.
From here on in this paper, unless stated otherwise, we use the term "haplotype" to denote a unique amino-acid haplotype. For clarity, the amino acid haplotypes are denoted TLR*aa. After translation, only two unique amino acid sequences were found both in TLR7 and TLR9. The less frequent allele of TLR7 was present only in four individuals, and of TLR9 only in one animal, thus we were not able to test for an effect of any of these loci using GLM models. To control for relatedness between individuals and possible population structure, we computed a pairwise relationship matrix based on six previously genotyped microsatellite loci  19  using the irelr package for R (https:// github. com/andersgs/irelr)  45. We summarized the variation using principal component analysis (PCA). The first and second principal components (PC1 and PC2) were fitted to each model.
Synonymous SNPs were filtered using the PLINK 1. 9 tool (https://www. cog-genomics. org/plink2)  49. We retained individuals with at least 30% of SNPs called, and SNPs that were in the Hardy-Weinberg equilibrium (P-value cutoff 0. 01) with less than 10% missing data and with minor allele frequency greater than 0. 01. Linkage disequilibrium among all SNP pairs was examined using squared allele-counts correlation (r 2 ), and SNPs were thinned out such that r 2 among SNP pairs within a sliding window of 50 SNPs (step-size 10 SNPs) was below 0. 7. Filtered SNPs were fitted as explanatory variables to GLM models constructed in a similar manner as in the case of haplotype-based models, with pathogen presence/absence as dependent variable, and genotype at a given SNP, two PCs summarizing the relatedness among individuals, and optional non-genetic terms as explanatory variables.
Acknowledgements The work was supported by grant no. DEC-2012/07/B/NZ8/00058  from the  Polish National Science Centre  to  A. K. Field  studies were funded by grant  MNiI   2P04C09827  " Badania naturalnych źródeł zarażenia mikropasożytów patogennych dla człowieka " to AB. We are thankful to  Dr. hab W. Babik  who provided access to an Illumina MiSeq platform, and to  K. Dudek  who prepared the Nextera library. Special thanks to  A. Biedrzycka  for her valuable comments on the final version of the manuscript. We also would like to thank two anonymous reviewers for their valuable comments that helped to improve the manuscript.
A polygon-offset of 0. 5 Å was used to account for 3D structure determination inaccuracies. These values were calculated by the DSSR tool package  (46). Typically, the fourth nucleotide of these turns adopts a syn conformation as in the UNCG loops embedding a CpG Z-step. Although rare variants with the fourth nucleobase in anti, called Z anti -turns, do exist (32), they were not considered in this study--see for example the E. coli cGC 2145 CAg with the adenine in anti. Note that this loop embeds atoms with B-factors above 80 Å2 and displays weak electron density as shown by the RNABricks2 web service  (47). The corresponding 'e-density' score is associated either with weak signal (below 1. 0 sigma on average) or real-space correlation coefficients value below 0. 7. Alike, a cUA 1535:DA CGg 'Z-turn' is capping H59 in subdomain-III.
Inspecting such sequences would have little impact on the characterization of UNCG and related Z-turn receptors. At the beginning of each of these files, one or two reference sequences (from structures with available 3D data, see Supplementary Table  S2 ) were added: H. marismortui to archaea (209 sequences), E. coli and T. thermophilus to bacteria (3735 sequences), S. cerevisiae and H. sapiens to eukarya (2341 sequences). LSU sequences were aligned with SSU-ALIGN (51), using a custom-built LSU template as reference for the alignments. This template was generated by SSU-build, a module of SSU-ALIGN, using the sequence family seeds RF02540 (LSU rRNA archaea, 92 organisms), RF02541 (LSU rRNA bacteria, 102 organisms) and RF02543 (LSU rRNA eukarya, 89 organisms) extracted from the Rfam. seed file (available on: ftp://ftp.
Yet, some interrogations remain that we could not fully address. Why are these H62 and H35a motifs so highly conserved in rRNA and why don't we observe a YNNG instead of a GAAA loop at the end of H35a? More surprisingly, why are covariations almost inexistent in the present rRNA sequence dataset? On a methodological note, our conservative approach for locating Z-turns may have led to the exclusion of a few Z-turns that might emerge as valid once better resolution structures will become available. However, it is worth noting that, aside applying resolution criteria, we did not consider the E. coli H59 UA 1535 CG Z-turn of the LSU subdomain-III (Figure  1A ; Supplementary Figure  S1 ) given that neither   2 ). The 'Y' receptor involves a Watson-Crick base pair between the second nucleotide of the Z-turn (cyan) and a complementary base pair of the receptor in green (Figure  2C ).
The 'R' receptor involves the formation of a A-minor motif between the second nucleotide of the Z-turn (cyan) and a G=C pair in green (Figure  2D ). For 'R' compared to the 'Y' receptors, it is more difficult to propose meaningful covariations. its fold, sequence nor stem length are conserved in available rRNA structures and that it adopts a deformed Z-turn structure that does not form RNA/RNA contacts, underlying the versatility of rRNA structures in non-conserved regions. Therewith, we are confident that Z-turn receptors are much less frequent than U-turn receptors in the RNA world. We anticipate that the so far exclusive presence of natural Z-turn receptors in rRNA may reflect the currently limited size of large RNAs and RNPs in structural databases. With ramping structural biology efforts towards atomic details of large RNPs and considering the length of many non-coding RNAs and their roles as transient scaffolds for protein binding, Z-turn receptors may emerge in other bi-.
ACKNOWLEDGEMENTS L. D. , Q. V. and P. A. wish to thank  Prof. Eric Westhof  and  Dr Pascale Romby  for their support. We also wish to thank  Dr Eric Nawrocki  and  Prof. Sean Eddy  for their kind help regarding  SSU-ALIGN , as well as  Dr John A. Hammond ,  Dr Daniel Eiler ,  Prof. Jeff Kieft ,  Prof. Norm Pace  and  Prof. Tom Cech  for their feedback on the manuscript.
, 2016; Cugola et al. , 2016; Merfeld et al. , 2017; Parra et al. , 2016). Unlike most other flaviviruses, a component of the spread of ZIKV may reflect its potential for human-to-human transmission. ZIKV is believed to disseminate using both sexual and non-sexual routes including body fluids such as tears, saliva or blood  (D'Ortenzio et al. , 2016; Motta et al. , 2016; Musso et al. , 2016). The risk of human infection by solid organ transplantation has also been suggested  (Alcendor, 2017). ZIKV contains a single-strand positive sense genomic RNA, which is translated into a large and unique polyprotein. The polyprotein is then processed by host and viral proteases into three structural proteins (C, prM/M and E), and seven nonstructural (NS) proteins (NS1 to NS5). Recently, we reported the generation of an infectious cDNA clone of MR766 (hereafter MR766 MC ) based on the GenBank access number LC002520 (MR766-NIID) after electroporation of four overlapping synthetic fragments that cover the genomic RNA sequence using the ISA method  (Atieh et al.
Design of ZIKV molecular clones The molecular clone design and production strategies for ZIKV were previously described by  Gadea et al. (2016). The viral clone BR15 MC was produced as described for MR766 MC  (Gadea et al. , 2016). BR15 MC was based on the sequence of epidemic ZIKV strain BeH819015 (Gen-Bank access KU365778) isolated in Brazil in 2015. MR766 was passaged more than hundred times in new-born mouse brain, and BeH819015 was isolated from blood patient and sequenced after one passage on C6/36  (Dick et al. , 1952; Faria et al. , 2016). Because the sequences of the untranslated regions (UTRs) deposited in GenBank were partial and also showed abnormalities, the 5'UTR of MR766 MC and the 3'UTR of contemporary clinical isolate Paraiba of ZIKV (GenBank access KX280026) were chosen to generate BR15 MC.
Quantification of viral stocks Zika virus samples were analyzed by titration on Vero cells while genomic viral RNA was quantified by RT-qPCR. For viral genome quantification, viral RNA was extracted from virus particles using QIAmp kit  (QIAGEN). The PCR standard curve used for the quantification of ZIKV copy numbers was obtained with a pUC57/ZIKV-E amplicon plasmid containing a synthetic cDNA encompassing nucleotides 961-1301 of genomic RNA (GenBank accession number LC002520). The couple of ZIKV E primers was used to equally amplify pUC57/ZIKV-E amplicon and the cDNA encompassing nucleotides 1046-1213 from genomic RNA of ZIKV molecular clones used in this study.
Flow cytometry assay A549 Dual cells were grown on 6-well plates at 5. 10 5 cells per well and infected at a multiplicity of infection (MOI) of 1. Infected cells were harvested and fixed with 3. 7% formaldehyde in PBS for 20 min, permeabilized with 0. 1% Triton-X100 in PBS for 4 min and then blocked with PBS-BSA for 10 min. Cells were stained with anti-E 4G2 (1:1000) for 1 h. Antigen staining was visualized with goat anti-mouse Alexa Fluor 488 IgG (1:1000) for 20 min. Cells were subjected to a flow cytometric analysis using FACScan flow cytometer (BD Bioscience). The percentage of positive cells was determined using FlowJo software.
RT-qPCR Total RNA including genomic viral RNA was extracted from cells (Qiagen) and reverse transcription was performed using 500 ng of total RNA, random hexamer primers (intracellular viral RNA) or E reverse primer (virus particles) and MMLV reverse transcriptase (Life Technologies) at 42 °C for 50 min. Quantitative PCR was performed on a ABI7500 Real-Time PCR System (Applied Biosystems). Briefly, 10 ng cDNA was amplified using 0. 2 μM of each primer and 1X GoTaq Master Mix (Promega). When appropriate, data were normalized to the internal standard GAPDH. For each single-well amplification reaction, a threshold cycle (Ct) was calculated using the ABI7500 program (Applied Biosystems) in the exponential phase of amplification. Relative changes in gene expression were determined using the 2ΔΔCt method and reported relative to the control.
Statistical analysis All values are expressed as mean ± SD of at least two experiments, or as mean ± SEM of triplicates. Comparisons between different treatments have been analyzed by a one-way or two-way ANOVA tests as appropriate. Values of p < 0. 05 were considered statistically significant for a post-hoc Tukey's test. All statistical tests were done using the software Graph-Pad Prism version 7. 01.
Acknowledgements We gratefully knowledge  P. Mavingui  and  REACTing consortium  for their support. We thank  J-J. Hoarau  for helpful discussions. This work was supported by the  ZIKAlliance  project ( European Union - Horizon 2020 programme  under grant agreement no  735548 ) and the  ZIKAlert  project ( POE FEDER 2014-2020 Ile de la Réunion action 1. 05  under grant agreement no SYNERGY: RE0001902 ). SB has PhD degree scholarship from  La Réunion Island University (Ecole Doctorale STS) , funded by  French ministry MEESR.
g. Bundy et al. 2007; Pracy et al. 2009; Kaviraj et al. 2013; Haines et al. 2015; Lofthouse et al. 2017). Nevertheless, while today's surveys are able to provide datasets of sufficient quality that it is possible for galaxy populations across a large range in redshift to be compared morphologically, an empirical determination of the role that mergers and other processes may play in the morphological evolution of galaxies remains difficult. For example, given the limited depth and/or survey area of past surveys, samples of mergers are typically small (e. g. Darg et al. 2010a,b). And since the surface brightness of tidal features induced by mergers decreases with the mass ratio of the merger (e. g. Peirani et al. 2010) , most surveys are too shallow to detect the signatures of low mass ratio mergers (see e.
ACKNOWLEDGEMENTS We are grateful to the anonymous referee for many constructive comments that improved the quality of this paper. GM acknowledges support from the  Science and Technology Facilities Council  [ ST/N504105/1 ]. SK acknowledges a  Senior Research Fellowship from Worcester College Oxford. JD acknowledges funding support from  Adrian Beecroft , the  Oxford Martin School  and the  STFC. This research has used the DiRAC facility, jointly funded by the  STFC  and the  Large Facilities Capital Fund of BIS , and has been partially supported by grant  Spin(e)   ANR-13-BS05-0005  of the  French ANR. This work was granted access to the HPC resources of CINES under the allocations 2013047012, 2014047012 and 2015047012 made by  GENCI. This work is part of the  Horizon-UK  project.
Understanding the formation and evolution of our Galaxy requires accurate distances, ages and chemistry for large populations of field stars. Here we present several updates to our spectro-photometric distance code, that can now also be used to estimate ages, masses, and extinctions for individual stars. Given a set of measured spectrophotometric parameters, we calculate the posterior probability distribution over a given grid of stellar evolutionary models, using flexible Galactic stellar-population priors. The code (called StarHorse) can acommodate different observational datasets, prior options, partially missing data, and the inclusion of parallax information into the estimated probabilities. We validate the code using a variety of simulated stars as well as real stars with parameters determined from asteroseismology, eclipsing binaries, and isochrone fits to star clusters.
Our main goal in this validation process is to test the applicability of the code to field stars with known Gaia-like parallaxes. The typical internal precision (obtained from realistic simulations of an APOGEE+Gaia-like sample) are 8% in distance, 20% in age, 6% in mass, and 0. 04 mag in A V. The median external precision (derived from comparisons with earlier work for real stars) varies with the sample used, but lies in the range of [0, 2]% for distances, [12, 31]% for ages, [4, 12]% for masses, and 0. 07 mag for A V. We provide StarHorse distances and extinctions for the APOGEE DR14, RAVE DR5, GES DR3 and GALAH DR1 catalogues.
2009 ), the Apache Point Observatory Galactic Evolution Experiment (APOGEE,  Majewski et al. 2017) , the RAdial Velocity Experiment (RAVE;  Steinmetz et al. 2006) , the Galactic Archaeology with HERMES survey (GALAH;  Martell et al. 2017) , the LAMOST Experiment for Galactic Understanding and Exploration (LEGUE,  Deng et al. 2012) , and the Gaia ESO Survey (GES; Gilmore 2012). The combination of such datasets with broad-band photometric data and the astrometric solutions from Gaia allow for a much more detailed modelling of the chemo-dynamical history of the Milky Way. On the one hand, Gaia's proper motions and parallaxes, complemented with radial-velocity measurements, enable us to measure stellar phase-space distribution functions with unprecedented precision over a Galactic volume of ∼ 8000kpc 3.
4, using simulated stars, focussing especially on the new parameters mass, age, and extinction. We also discuss how biased spectroscopic parameters influence the estimated quantities. In Sec. 5 we compare our distances to several previous mass, age, and distance determinations that can be used as a reference. In Sec. 6 we describe a 1 http://sci. esa. int/gaia/ StarHorse application to a few spectroscopic surveys, with the purpose of delivering public releases of distances and extinction, We refrain from releasing ages and masses for the time being since their accuracy is still dependent on availability of Gaia-DR1 parallaxes and additional improvements. Gaia-DR2 will certainly improve their application to large volumes and datasets. We conclude the paper with a summary and future plans in Sec.