dataesr/scientific-paragraphs-categorization huggingface dataset:
5 mM MgCl 2 , 5%DMSO, 0. 25 μM primers). The fluorescently-labelled PCR product was loaded on an ABI 3730 capillary sequencer and analysed as a variable length fragment polymorphism using GenScan size standard ROX-500 as internal size standards. Data were collected and visualised with Genotyper Software v3. 7. To determine the number of repeats that corresponds to each allele identified in the genotyping assay, the PCR products from 6 homozygous individuals were Sanger sequenced. The sequences of all PCR primers, HRM probes, TaqMan probes, and all PCR conditions are available from the authors on request. The proportion of successfully genotyped DNA samples was 95. 1% for rs944289 (near NKX2-1), 90. 3% for rs965513 (near FOXE1), 79. 4% for rs1867277 (5'UTR of FOXE1), 93. 6% for rs71369530 (poly-alanine expansion in FOXE1) and 98.
All statistical analyses were done with the SAS software, version 9. 3 (SAS Institute Inc, NC, USA). Age distribution of study participants, at time of thyroid cancer diagnosis or corresponding age for controls, was ranged from 10 to 62 and was divided into seven homogeneous classes, which were then used along with gender to stratify the cases and controls for statistical analyses. Two groups were designed for ethnic origin: those individuals with two Polynesian parents and others. For BMI, BSA, and dietary iodine intake, the medians of the distributions among the female genotyped controls (25. 7 kg/m 2 , 1. 8 m 2 , 132. 2 μg/day, respectively) and the medians of the distributions among the male genotyped controls (30. 1 kg/m 2 , 2. 1 m 2 , 146. 9 μg/day, respectively) were used as limits to create two groups among the participants in our study.
Acknowledgments The authors thank  John Paoaafaite  and  Joseph Teuri , who contacted and interviewed cases and controls for the study. Finally, they also thank  P. Morales ,  J. Iltis ,  P. Giraud ,  P. Didiergeorge ,  M. Brisard ,  G. Soubiran ,  B. Caillou ,  P. Dupire ,  J. Ienfa ,  G. de Clermont ,  N. Cerf ,  B. Oddo ,  M. Bambridge ,  C. Baron ,  A. Mouchard-Rachet ,  O. Simonet ,  D. Lamarque ,  J. Vabret ,  J. Delacre ,  M. P. Darquier , and  J. Leninger , for their help in the collection of the cases or in the organisation of the case-control study.
After a short description of the station, the detailed setup of the GC as well as the calibration strategy are addressed. A paragraph is dedicated to data quality control and atmospheric measurement comparisons, showing to what extent our measurement system are in line with the WMO recommendations (WMO-GAW, 2013). In the last part, we present and analyze our 3-year time series of ambient air measurements. Finally, we demonstrate that these time series can be used to estimate the monthly regional fluxes of CO 2 , CH 4 and N 2 O in the catchment area of the Puy de Dôme station, using radon-222 as an atmospheric tracer.
The PBL height analysis revealed that the Puy de Dôme station is in the free troposphere during more than 70 % of the time and up to 81 % of the time during winter. Back trajectories were calculated using the Lagrangian dispersion model Flexpart version 8. 2. 3, based on ECMWF ERA-Interim data at a horizontal resolution of 1 • × 1 • , with 60 vertical levels and a 3-hour temporal resolution. Eight particles were released every 15 min (96 particles every 3 h) in a 3-D box centered around the Puy de Dôme station (from lower left corner 45. 76 • N, 2. 95 • E to upper right corner 45. 78 • N, 2. 97 • E; between 1400 and 1500 m a. s. l. ) with a lifetime of 3 days. This simulation was performed for particles arriving at the station between 1 January 2010 and 31 December 2012. The footprints were computed on a 1 • × 1 • horizontal grid, following the method described by  Lin et al.
Analysis management Data acquisition, valve opening and closing and temperature regulation of the GC system are entirely processed by Chemstation software  (version A. 10. 02, Agilent). This software allows controlling the GC system parameters through the socalled "methods". A typical method is configured to do the following: -control the temperature of the detectors, the catalyst and the oven; -regulate the flows of the sample (via Aux5), the carrier gases (via Aux3 and Aux4), H 2 and zero air, all via five distinct EPCs; -schedule the opening and closing of the four six-port two-way Valco valves, controlled via the internal events output GC connector; -choose the position of the eight-port microelectronic Valco valve, controlled via the external events output GC connector; and -integrate the results of the analysis (via the chromatograms).
Xia et al. (2010)00)ions of GC working standards used during period A (July 2010 to April 2013) and period B (May and June 2013). Ambient air is dried in a distinct cooling trap maintained in the same ethanol bath as the GC trap (seeSect. 3. 1). The flasks are then shipped and analyzed at LSCE by a GC system for CO 2 , CH 4 , N 2 O and SF 6 and by a Finnigan MAT-252 isotope mass spectrometer for CO 2 isotopic composition ( 13 C and 18 O). The calibrations for trace gas analysis are performed in the same manner as presented in Sect. 3. 1. 3,andtheresultsarestoredinthesamedatabase. Radon-222 ( 222 Rn) is a radioactive noble gas (T 1/2 = 3. 8 days) and is part of the radioactive decay chain of uranium-238. Uranium-238 in the earth's crust results in the emission of222Rn by the earth's surface.
Acknowledgements. The authors thank the  LaMP team  for weekly maintenance of the station and weekly flask sampling. We also acknowledge the support provided by the  RAMCES group at LSCE  for the regular maintenance of the analyzer and for data management. We thank the  ECWMF  and  I. Pison  for providing and extracting the meteorological conditions at the  Puy de Dôme pixel. We also want to thank  I. Levin  and  U. Karstens  for providing and sharing the European 222 Rn map emissions. We used the marine boundary layer reference kindly provided by  E. Dlugokencky  and  K. Masarie  from  NOAA. The authors acknowledge the  Regional Council of Auvergne  for financial support. This work was partly funded by the  InGOS EU  project ( 284274 ). Edited by:  P. Herckes.
All variables have strong positive first order auto-correlations. 27  The right panel of figure  5  shows the cross-correlations between the cyclical component of real output at time t and of real output, unemployment, real investment, and real consumption at time t -lag. The position of the peak in each correlation figure indicates whether the variable is lagged, coincident or leading with respect to output. The shape, dimension and the position of the peak of our simulated time series provide a very good fit of the properties shown by real time series. The fundamental properties of our simulated time series highlighted above are robust across the experiments performed in the sensitivity analysis, 28 though the absolute values of the correlations slightly change. In particular, investment always appears to be pro-cyclical and coincident with real output, unemployment is counter-cyclical and lagged by a quarter, while consumption is always pro-cyclical and 26 Real time series are taken from the Federal Reserve Economic Data (FRED): they are quarterly data ranging from 1955-01-01 to 2013-10-01 for unemployment (not seasonally adjusted, FRED code: LRUN64TTUSQ156N) and ranging from 1947-01-01 to 2013-10-01 for investments, consumption and GDP (FRED codes: PCECC96, GPDIC96, and GDPC1 respectively).
27 A remarkable difference, however, has to be found in the 20 th auto-correlation of investment and unemployment which look significantly higher than real ones. This inconsistency is easily explained in light of our assumption that real capital has a duration of 20 periods before being scrapped off. This introduces a significant cyclical component in real investment. 28 For space reasons we omit the figures concerning the sensitivity scenarios. These are available online at: https://github. com/S120/benchmark may be either coincident or slightly lagged by a quarter according to the specification employed. This suggests that the fundamental properties of the correlation structure observed in artificial data is an inherent property of the model rather than being dependent on a specific parametrization.
Stanley et al. (1995)  confirm that, like in earlier studies, the log-normal distribution fits the data well except for the upper tail, which contrary to the common belief are thinner rather than fatter. On the same line  Cabral and Mata (2003)  finds that the log-normal fits well data for firms with publicly available accounting data, while using more complete datasets the firm size distribution is more right-skewed, though evolving over time toward a more symmetric one if we consider firm size distribution for groups of different ages. Bottazzi and Secchi (2003)  finds that the empirical densities of US firm size distributions display a remarkable degree of stationarity over time and are very well approximated by a log-normal fit, confirming the results of  Stanley et al. (1995). Axtell (2001)  on the contrary finds that firm size distribution in the US can be approximated by Zipf distribution, that is a Pareto distribution with unitary exponent.
(2004); Delli Gatti et al. (2004). 44 Admittedly, several other parameters are likely to exert an impact, such as the rate of taxation, the threshold employed in workers reservation wage function, or households' propensities to consume. However, besides space and tractability reasons, let us point out that the values of these parameters have been jointly derived (see appendix A. 1) in order to be compatible with the SFC initial setup. Changing their values would then impose an exogenous shock on the model or require to change the initial configuration of stocks and flows, in both cases preventing a direct comparison with the baseline calibration. 45 The figures and tables summarizing the results of the five sensitivity experiments can be found on the platform web page: https://github. com/S120/benchmark.
Local realignment around indels was performed using the Genome Analysis Tool Kit (GATK) v1. 6-2 (DePristo et al. , 2011). Single-nucleotide polymorphism genotypes were called using the GATK v1. 6-2 UnifiedGenotyper with default parameters with the exception of setting expected heterozygosity to 0. 015. We applied positional filters to exclude repetitive regions of the genome  (Heliconius Genome Consortium, 2012). Filters for coverage (410 × and o200 × /249 × for offspring/parents, respectively), genotype (GQ ⩾ 30) and mapping quality (MQ ⩾ 40) were applied using a custom Perl script (Kanchon Dasmahapatra, pers. comm. ). After filtering, markers with genotype calls at fewer than 80% of individuals were excluded. Sites showing Mendelian inconsistencies were removed and missing genotype calls were imputed, scaffold-by-scaffold, using Beagle v.
Linkage groups corresponding to the 20 Heliconius autosomes were reconstructed using female informative markers and a logarithm of odds (LOD) threshold ⩾ 6 for all three data sets. The sex chromosome was not reconstructed. Male informative and intercross markers were collapsed to unique segregation patterns using a custom Perl script (John Davey, pers. comm. ). Collapsed markers were combined with female-informative chromosome prints and clustered by linkage group (LOD ⩾ 5). Individual linkage maps were built using the Kosambi mapping function and a LOD ⩾ 1.
Mapping wing pattern loci Phenotypes segregating with discrete alternative states were incorporated directly into map construction, alongside the collapsed marker sets, and were thus co-localised with the markers with which the phenotype was most strongly associated. A generalised linear model was used to test for an association between the number of spots on the margin of the hindwing and each of the 20 'chromosome prints', using R. This analysis was possible as the mother of the brood (and not the father) was heterozygous for this trait. The overall colour pattern variation quantified with Colour Pattern Modelling was mapped as quantitative trait locus (QTLs) by using a genome-wide Haley-Knott regression implemented in the R/qtl package  (Broman et al. , 2003). This analysis was extended by performing multivariate analysis on all principal components with an eigenvalue ⩾ 2% using the R/shapeQTL package (available on request; see Supplementary Methods for more details).
Statistical thresholds for significant linkage were based on 1000 permutations. To further evaluate the identified QTLs, we ran a stepwise multiple QTL model search using the algorithm developed in  Broman and Sen (2009)  and implemented in the R/ shapeQTL package for multivariate traits. The search was restricted to additive QTLs but epistatic interactions between discovered QTLs were evaluated in the final model. As colour pattern analysis is affected by sex  (Jones et al. , 2011) , we used gender as an additive covariate. We employed only male informative markers on the 20 dense autosomal linkage maps for the different broods. The subsets of offspring in the final linkage maps having intact wings were used for this analysis. A conventional threshold of LOD ⩾ 3 and other relaxed requirements were used to detect suggestive QTLs (Supplementary Methods).
ACKNOWLEDGEMENTS We thank  Adriana Tapia ,  Andrés Orellana ,  Cristóbal Ríos ,  Catherine Brunton ,  Moisés Abanto ,  Elizabeth Evans ,  Nicola Nadeau  and  Patricio Salazar  for help with butterfly collection and breeding; the  Autoridad Nacional del Ambiente (Panamá)  for collection permits; Lise Frézal, The  Gene Pool (Edinburgh Genomics) , the  BoEM lab (MNHN)  and the  SSM (MNHN)  for assistance with molecular work;  Marcus Kronforst  for providing primers;  John Davey ,  Kanchon Dasmahapatra ,  Manmohan Sharma  and  Robert Jones  for assistance with sequence analysis and bioinformatics;  Violaine Llaurens  and  Chris Jiggins  for help and comments on the manuscript. We are most grateful to the three anonymous referees whose in-depth reviews improved this work. This work was funded by a  Smithsonian Tropical Research Institute  short-term fellowship to BH, AM and BG and by a  CNRS   ATIP grant , an  ERC grant  ( MimEvol)  and an  ANR grant  (HybEvol) to MJ.
Alignment of the Evaluation Dataset The test data contains four parallel documents in English, Spanish and Italian. Our system exploits the parallelism of the test set, a feature overlooked by previous systems  (Navigli et al. , 2013). In order to avoid some discrepancies observed at the level of sentence correspondences, we first align the texts pairwise using the Hunalign sentence aligner  (Varga et al. , 2005). Then we run GIZA++  (Och and Ney, 2003)  in both directions at the lemma level and retain only intersecting alignments to rule out spurious correspondences. For each instance of an English content word in the test set we identify its Spanish translation in context and, alternatively, the English translations of Spanish and Italian words. We use the lemma and part-of-speech information provided by the task organizers.
This result is very interesting given that our method is very simple: it needs no training and is very easy to compute as it only relies on alignment and sense ranking. Note that the BFS baseline for English is a very strong one that none of the systems manages to beat. As the test set is very small (∼ 138 parallel sentences), we expect the method to perform even better on larger corpora where the automatic alignment will have higher accuracy and coverage. Our system performs poorly in Spanish and Italian in comparison to English, and is ranked in the fourth position. The scores obtained in these languages are given in Table  2  and are compared to the best performing system and the baseline. A close analysis of the results reveals that the weaker system performance is due to the way the BabelNet API carries out sense ranking in these languages.
In English, WordNet senses are ranked first sorted by sense number 7  and are followed by Wikipedia senses in lexicographic order  (Navigli, 2013). For languages other than English where frequency information is not available, senses are sorted in lexicographic order, 8  a criterion that often fails to reflect their relevance (i. e. rare senses might be placed higher than more frequent ones). This certainly affects our system which relies on sense ranking a) when multiple senses are retained after filtering by alignment, and b) when the BFS is needed. 9 he low values of the Spanish and Italian BFS baseline reported by the task organizers confirm this finding. As the first sense retained by the Babel-Net API in these languages often is not the most frequent sense, the baseline is outperformed by almost all participating systems.
Genome reconstructions Genomic DNA was extracted using the NucleoBond â AXG provided by Macherey-Nagel (www. mn-net. com) as previously performed for the CyanoGEBA  (Shih et al. 2013). Sequencing of eight cyanobacterial genomes was conducted at the Transcriptomics Facility of the University of Bristol using an Illumina Genome Analyzer IIx and Illumina HiSeq 2500. Paired-end reads were 100 bp in length and coverage for all genomes was >1009 coverage. From the resultant sequences, low-quality reads, sequencing adapters and primers were removed using the adapter cleaning program Fastq-mcf (Aronesty 2013). An initial de novo assembly of newly sequenced cyanobacterial genomes was conducted applying Velvet v1. 2. 10 software  (Zerbino and Birney 2008). Optimal K-mer lengths for a sensitive, yet specific assemblage, were determined separately for each genome based on highest N50 values and maximum contig length.
Ancestral character state reconstruction Ancestral character states were reconstructed using the 'ace' function of the ape package  (Paradis et al. 2004 ) in R (http://www. r-project. org. /). Ancestral states were reconstructed applying maximum likelihood methods assuming equal and asymmetrical transition rates. q01 describes the transition to multicellularity and q10 the transition to unicellularity. Character states were reconstructed using continuoustime Markov models as implemented in BayesTraits v. 2 beta  (Pagel et al. 2004). Markov chain Monte Carlo (MCMC) analysis were run for 1 010 000 iterations, with a default burn-in of 10 000. Convergence of all parameters was reached at this point. Transition rates q01 and q10 were sampled from a uniform prior distribution with the assumption that any rate within this interval is equally likely.
Divergence time estimation and calibration Divergence times were estimated applying relaxed clocks with lognormal rate distribution, which incorporates lineage-specific rate heterogeneity  (Drummond et al. 2006). Previous studies have confirmed: (1) the monophyly of the phylum Cyanobacteria; and (2) Gloeobacter violaceus closest to a eubacterial outgroup  (Blank and Sanchez-Baracaldo 2010; Schirrmeister et al. 2011b; Shih et al. 2013). The position of G. violaceus outside a cyanobacterial ingroup is further supported by morphological and genetic differences, such as the lack of thylakoid membranes  (Rippka et al. 1974 ) and many missing genes associated with the photosystems  (Nakamura et al. 2003). Divergence time estimation was conducted using the software MCMCTREE, part of the PAML 4. 4b package (Yang 2007), and run on the High Performance Computing machine (Bluecrystal) at the Advanced Computing Research Centre of the University of Bristol.
Branch lengths were estimated using the software CODEML, also part of the PAML package. Again, LG + Γ (Le and Gascuel 2008) was selected as the model of evolution. Based on the estimated substitution rates, we used a gamma distribution with a shape parameter of 1 and a scale parameter of 9. 53, as prior for the overall substitution rates. In a first run without calibration priors, the gradient vector and Hessian matrix were calculated. For each analysis, we ran four Markov chain Monte Carlo analyses for 10 million generations, sampling every 100 generations; 50% were removed as burn-in. At this point, all parameters had reached convergence. Using divergence time estimation, two hypotheses were investigated with different combinations of three calibrations (Table  1 ). Userspecified time priors and the 'effective' (joint) time prior implemented in the calculation of the posterior ages may differ substantially  (Warnock et al.
Acknowledgements. We acknowledge with thanks the help of  Alan McNally ,  Jane Coghill ,  Christy Waterfall ,  Al Tanner ,  Davide Pisani  and  Mario dos Reis. Particular thanks goes to  Callum Wright  for support with the Bluecrystal high computing machines. We thank the  Institut Pasteur for funding of the Pasteur Culture collection of Cyanobacteria , as well as  Thierry Laurent  and  Th er ese Coursin  for cyanobacterial maintenance and genomic DNA. Additionally, we would like to thank two anonymous reviewers for helpful comments and interesting discussion points. We further thank the  Palaeobiology group  at the  University of Bristol  for thoughtful comments. BES would like to thank the  Palaeontological Association  and  Crispin Little  for the invitation to contribute to the symposium at the 58th Annual Meeting of the Association in Leeds.
DATA ARCHIVING STATEMENT Sequenced genomes in this study are part of the bio-project PRJEB9564 in the European Nucleotide Archive (ENA). Data of eight cyanobacterial genomes are available in GenBank and MicroScope: www. ncbi. nlm. nih. gov/genbank/ www. genoscope. cns. fr/agc/microscope/ Additional data for this study are available in the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. h75ht.
Microarray data are available in the ArrayExpress database (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-3925. Table  1. Number of genes that were differentially expressed upon treatment with 10 µg/mL LMWF. ECFCs and HUVECs were treated with 10 µg/mL of LMWF for 6 h or 24 h. RNA was extracted and analyzed using Illumina Human HT-12 Expression beadchips. After filtering probes with detection p-values < 0. 05 for at least half of the arrays, differentially expressed genes with an expression fold change greater than 1. 5 fold were selected using the Student test, with a significance p-value < 0. 05, including Bonferroni and Benjamini Hochberg false discovery detection. Each dataset was derived from at least four biologically independent replicates. Table  2. Functional analysis of differentially expressed genes upon treatment with 10 µg/mL LMWF.
ECFCs and HUVECs were treated with 10 µg/mL of LMWF for 6 h or 24 h. RNA was analyzed using Illumina Human HT-12 Expression beadchips. Differentially expressed genes with an expression fold change greater than 1. 5 fold were selected using the Student test, with a significance p-value < 0. 05. Each dataset was derived from at least four biologically independent replicate samples. The generated lists were interpreted using the Ingenuity interface. A selection of the most relevant functions that were modulated in most conditions is represented below, along with the number of differentially expressed genes (n) for each function, and the corresponding p-value range.
Western Blot Analysis ECFCs and HUVECs were seeded in 6-well 0. 2% gelatin coated plates at a density of 2 ˆ10 5 cells/well. Two days later, when cells reached subconfluence, they were placed overnight in EBM-2 + 5% FCS. The next morning, cells were washed with PBS and serum starved for 4 h in EBM-2. Cells were then stimulated with 10 µg/mL of LMWF or LMWH. After 5, 10, 15, 45 or 120 min of stimulation, wells were washed with PBS and cells were lysed in NuPAGE lysis buffer. Equal amounts of protein from each group were resolved by SDS/Page on a 8% acrylamide gel and probed by immunoblotting using anti-pAKT and anti-AKT antibodies (Cell signaling, Danvers, MA, USA). Densitometric readings were obtained using ImageJ software. Results are expressed as the ratio of pAKT to total AKT densitometric readings and represented as means ˘SEM of at least three independent experiments.