dataesr/scientific-paragraphs-categorization huggingface dataset:
The screening attendance of all women born between 1 January 1988 and 30 September 1993 in the year after their 20th birthday was obtained from SCCRS. This was based upon an extract in Q1 2015 that had validated data up to the end of Q3 2014. Consequently, the 1993 birth cohort is truncated to ensure this cohort has at least 12 months follow-up. The information included: date invited for screening, date attended/reminded/defaulted as appropriate, if excluded from screening, and reason for exclusion, CHI, postcode of current residence recorded by registered general practitioner, number of doses of vaccine administered. Women in the data set were classified as those eligible for the catch-up vaccination campaign and those not (those born before September 1990) according to their date of birth.
Data linkage. The CHI registry data set was used to identify the population in SCCRS that were resident in Scotland at age 20 years and to eliminate any duplicate CHI records created in error, to record attendance for the same individual. Once duplicate records had been merged with retention of relevant data, women with legitimate exclusions were removed in order to obtain an accurate denominator for the eligible population. These exclusions included 'Not clinically appropriate', death, transferred out of Scotland, and temporarily excluded for a co-morbidity or for being pregnant. The postcode of residence was used to generate a deprivation code (Scottish Index of Multiple Deprivation SIMD 2012 version), and indices of rurality (Scottish Government Scottish Index of Multiple Deprivation http://www.
scotland. gov. uk/Topics/Statistics/ SIMD) Deprivation is divided into quintiles, with SIMD1 being the most deprived and SIMD5 being the least deprived. Rurality is divided into three categories, urban (population of 410 000), accessible remote (30-60 min travel time from an urban centre) and very remote (460 min travel time from an urban centre). Following data linkage, the data were anonymised by replacing the CHI number with a unique study number. Statistical analysis. The influence of characteristics of 20-year-old women on their likelihood of attending for screening was estimated through logistic regression, with a log link. The unadjusted and adjusted risk ratios of attendance by year of birth cohort, SIMD, number of vaccine doses (0-3) and rurality were estimated. The primary data analysis was based upon all women resident in Scotland at age 20 years and who were eligible for invitation to screening.
Data quality is actively managed through the programme. The CHI number allows direct and robust linkage of many aspects of an individual's health record. The use of a national screening database means that the sample size is substantially larger than most previous studies. Although the Swedish study of Herweijer and colleagues was larger overall, there were significantly fewer immunised women  (Herweijer et al, 2015). One of the main limitations of this study is that the women analysed may be a different population, with different motivation, from women immunised routinely at age 12 or 13. Paynter et al have reported that although uptake in recently immunised women is better than unimmunised women of the same age, this effect diminishes as the time between immunisation and eligibility for cervical screening increases  (Paynter et al, 2015).
ACKNOWLEDGEMENTS We acknowledge funding (reference  CZH/4/528 ) from the  Chief Scientist Office  (part of the  Scottish Government Health and Social Care Directorates) , which has supported this programme of work. This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 4. 0 Unported License.
g. Dye et al. 2014) ; (iii) the statistics of lenses, as well as the distribution and extent of the image separations, depend on the darkmatter and dark-energy content of the Universe; therefore large samples of gravitational lenses can be used to constrain cosmological parameters (e. g. Grillo et al. 2008; Eales et al. 2015). Here we present the sample of candidate lensed galaxies with F500 100 mJy extracted from the ∼600 deg 2 of the full H-ATLAS. The work is organized as follows. The H-ATLAS catalogue and available ancillary data sets are presented in Sec. 2. In Sec. 3 we describe the selection of the candidate lensed galaxy and discuss the properties of the sample. In Sec. 4 we derive the number counts of lensed galaxies and compare them with model predictions. Conclusions are summarized in Sec.
Some of these sources have also been observed with the Submillimeter Array at ∼0. 5 resolution at 880 µm  (Negrello et al. 2010; Bussmann et al. 2012 Bussmann et al. , 2013)) , while six of them have been recently imaged in band 7 with the Atacama Large Millimeter Array (ALMA) with better resolution and sensitivity (PI: Eales). A lens modeling of the ALMA data will be presented in future papers  (Dye et.
2000)  to inspect available optical, infrared and radio imaging data and to query the NASA/IPAC Extragalactic Database 3 (NED) around the position of each source. We identified 11 blazars, according to their intense radio emission (F1. 4GHz > 100 mJy) and the rising/flat SED from sub-millimeter to radio wavelengths. They are listed in Table  1. Their number density is consistent with theoretical expectations (e. g. Tucci et al. 2011). Two of them were previosuly reported by Lopez-Caniego et al. (  2013 ) who 3 http://ned. ipac. caltech. edu/ searched for blazars in 135 deg 2 of the H-ATLAS equatorial fields. 231 spiral-like galaxies with an angular size exceeding several arcseconds in optical images were categorized as local galaxies. All were confirmed as local galaxies via available spectroscopic redshift.
g. Dye et al. 2014). Magnification factors, µ, have been derived from the lens modeling of the high resolution imaging data shown in Fig. 3. Those obtained at sub-millimeter wavelengths from SMA (and ALMA for HATLAS090311. 6+003907 and HATLASJ142935. 3-002836) data are reported in Table  4. For three objects the magnification factor derived by  Bussmann et al. (2013)  has been replaced by a lower limit: it is the case of the two cluster-scale lenses, i. e. HATLASJ141351. 9000026 and HATLASJ132427. 0+284449, which were modelled by Bussmann et al. as if they were galaxy-scale lenses, and SDP. 17, for which SMA did not resolve the multiple images. The measured values 4  of µ are in the range µ ∼ 5 -15, consistent with expectations  (Negrello et al. 2007 ). As we will discuss in section 4, the statistics of lensed galaxies (i.
Acknowledgments We thank the anonymous referee for helpful suggestions and useful comments. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement No  707601. This work was supported by  ASI/INAF Agreement 2014-024-R. 0 , by  PRIN-INAF 2012 project  " Looking into the dust-obscured phase of galaxy formation through cosmic zoom lenses in the Herschel Astrophysical Large Area Survey ". J. G. N. acknowledges financial support from the  Spanish MINECO  for a  Ramon y Cajal fellowship  ( RYC-2013-13256 ) and the I+D 2015 project  AYA2015-65887-P (MINECO/FEDER ). L. D. , R. J. I. and S. J. M. acknowledge support from the  European Research Council  Advanced Investigator grant, COSMICISM. L.
Phenotype of Cultured Perivascular Stem Cells Cells were purified using FACS and cultured for between four and six passages in monolayer culture before analysis. Single cells were gated and histograms of each antibody were compared with the relevant isotype control (Fig. 4 ). Endothelial, hematopoietic, and myogenic cell markers were all absent (CD144, CD45, and CD56 respectively; data not shown). Each of the cell types was positive for the MSC markers CD44, CD90, and CD105. CD34 is not expressed in cultured cells and was absent in all of the tested groups. CD146 is normally expressed by pericytes and latepassage adventitial cells (unpublished data) but was absent in all of the groups. The compensation beads used to set up the experiment had been strongly positive, demonstrating that the anti-CD146 antibody was working and the FACS was set up correctly.
Subsequent analysis of cultured pericytes (passages 4-6) from 5 separate patients did not demonstrate any CD146 expression compared with isotype controls (data not shown). Cultured pericytes demonstrated expression of CD146, NG2, and PDGFRb with immunocytochemistry, compared with controls (Fig. 4 ). Extracted RNA was reverse transcribed and demonstrated expression of CD146, NG2, and PDGFRb on gel electrophoresis after PCR (Fig. 4 ). The RNA-free control as well as the CD31, CD34, and CD45 reactions were all negative. This demonstrated that cultured pericytes were CD146+ by immunocytochemistry and PCR, but not by FACS.
There are more than 20 clinical trials using MSCs for articular cartilage damage registered with the ClinicalTrials. gov website  [31]. Our data suggest that PSCs are, in fact, superior to MSCs and that current clinical trials are using suboptimal, uncharacterized cells, which might be a reason for the heterogeneous results reported in the literature with animal and human studies  [32]. Further work is required to define the optimal cells and their preparation for chondrogenesis if translation to patients is to be given the best chance of being effective.
In the past 5 years, RNA-Seq has become a powerful tool in transcriptome analysis even though computational methods dedicated to the analysis of high-throughput sequencing data are yet to be standardized. It is, however, now commonly accepted that the choice of a normalization procedure is an important step in such a process, for example in differential gene expression analysis. The present article highlights the similarities between three normalization methods: TMM from edgeR R package, RLE from DESeq2 R package, and MRN. Both TMM and DESeq2 are widely used for differential gene expression analysis. This paper introduces properties that show when these three methods will give exactly the same results. These properties are proven mathematically and illustrated by performing in silico calculations on a given RNA-Seq data set.
INTRODUCTION In the past 5 years, RNA-Seq approaches, based on high-throughput sequencing technologies, are becoming an essential tool in transcriptomics studies (cf. Wang et al. , 2009). It is now commonly accepted that a normalization preprocessing step can significantly improve the quality of the analysis, in particular, for the differential gene expression analysis (cf. Bullard et al. , 2010). Nevertheless, a gold standard normalization method has not yet been found. This paper deals with two widely used and very important normalization methods and a third method related to these. The first method is the "Trimmed Mean of M-values" normalization (TMM) described in  Robinson and Oshlack (2010)  and implemented in the edgeR package (cf. Robinson et al. , 2010). The second method is the "Relative Log Expression" normalization (RLE) implemented in the DESeq2 package (cf.
Tomato's RNA-Seq Data Set To investigate the tomato transcriptome dynamics of fruit set, RNA were isolated from flower buds (Bud) and flowers at anthesis (Ant) and post-anthesis (Pos) stages. For each stage, cDNA libraries were generated from three biological replicates and subjected to Illumina mRNA-Seq technology sequencing. Then, after mapping reads to the tomato genome sequence, we obtained a table of raw counts with 34675 rows (genes) and 9 columns (3 stages and 3 replicates per stage). These technical procedures are described in  Maza et al. (2013). In this paper, for sake of simplicity, the matrix (34675 × 9) containing raw counts is denoted by X.
Computations with R Packages All computations were done within R environment (cf. R Development Core Team, 2011). All packages are available from R or Bioconductor websites (cf. Gentleman et al. , 2004). As described above, the matrix containing raw counts is denoted by X in all R command lines of given in silico examples. The TMM normalization method is implemented in the edgeR package by means of the calcNormFactors function. For example, the default normalization factors obtained in Table  1  are obtained by the following command line: > calcNormFactors(X) The RLE normalization method is implemented in the DESeq2 package by means of the function estimateSizeFactorsForMatrix. For example, the   default size factors obtained in Table  1  are obtained using the following command line: > estimateSizeFactorsForMatrix(X) • • • • • • • • TMM RLE MRN Bud Ant Pos The MRN normalization method is implemented in a homemade function called mrnFactors which is provided as an additional file (see Additional Files).
Description of the Three Methods Table  2  gives us the description of these three normalization methods. Both TMM and RLE are respectively implemented in edgeR and DESeq2 packages (see Materials and Methods). Differences and commonalities of these three methods are described here, step by step. Step I Both TMM and MRN methods have a step of prenormalization of counts by library sizes. The RLE method does not and works directly on raw counts. Step II The three methods have a reference sample. For TMM, the reference sample has been chosen here arbitrarily with k = 1 and r = 1. The default for the edgeR package consists in choosing the library whose upper quartile is closest to the mean upper quartile for all the libraries. For MRN, the condition k = 1 is also arbitrarily chosen, as in  Maza et al.
ACKNOWLEDGMENTS I thank  Sarah Chrisment  for the thorough revision of my manuscript which help me to fix the grammatical errors and to improve the overall readability of the text. This work was supported by the  Laboratoire d'Excellence (LABEX)   TULIP  ( ANR-10-LABX-41 ). This work benefited from the networking activities within the European funded  COST ACTION FA1106 "Qualityfruit. ".
Meat quality score Each muscle, cooked with a specified cooking method, was assessed by 10 individual untrained consumers. The highest and lowest two scores for each muscle were removed and the average was calculated for the remaining six scores. These clipped mean values for tenderness, juiciness, flavour liking and overall liking were weighted and combined to create a single meat quality score (MQ4). The weightings were calculated using a discriminant analysis, as performed by  Watson et al. (2008)  and are 0. 3*tenderness, 0. 1*juiciness, 0. 3*flavour liking, and 0. 3*overall liking. There is a high correlation between all four sensory scores with a minimum partial correlation coefficient between any of the scores of 0. 66 calculated on a subset of the data  (Bonny et al. , 2015).
Statistical analysis The effect of both ossification score and age on the composite MQ4 score was assessed across the full ranges of ossification score and animal age in the dataset. The relationship between these two measurements and the MQ4 score was also explored within groups of carcasses with lesser or greater maturity. The first release of the MSA model used commercially in Australia disqualified any carcass with an ossification score greater than 200. To align with this the 'less mature group' was limited to animals that had an ossification score of 200 or less, or were less than or equal to 987 days old at slaughter, the age equivalent to an ossification score of 200 within this dataset (Figure  1 ). All carcasses not meeting these criteria were allocated to the 'greater maturity range.
All analyses were performed on the whole dataset and on these two subgroups using a combination of both correlation analyses and regression analyses. Using a dataset with only one observation per carcass, the partial correlation coefficients between ossification score and age for the three maturity range was determined with a bivariate model (SAS v9. 1) accounting for the fixed effects of country, sex, breed class and kill group, and significant interactions between these terms. A similar approach using a bivariate model was then taken to determine the partial correlation coefficients between the maturity measures and MQ4 within each of the three maturity ranges, accounting for the fixed effects of country, sex, breed class and kill group, and significant interactions between these terms. Initially this was done within each muscle separately, enabling individual partial correlations to be determined for each muscle.
This process was then repeated, but utilising data from all muscles combined in the one data-set with "muscle" included as a fixed term within the model. This enabled estimation of the mean partial correlation across all muscles. The composite score MQ4 was analysed using a linear mixed effects model (SAS v9. 1). Initially, a base model for all three maturity ranges was established, with the following fixed effects and all their significant interactions, carcass hanging method, cooking method, muscle type, sex, country, and breed class. Post-mortem ageing period in days was included as a covariate. Animal identification number within carcass source country, kill group (animals slaughtered on the same day at the same abattoir) and consumer country were included as random terms. The inclusion of animal identification number assumes that the correlation between eating quality scores in different muscles within the same animal are equal.
Correlation between maturity measures Aligning well with the hypothesis, ossification score and animal age had strong positive partial correlation coefficients across all the data and for the group with greater maturity. Although still positive, the correlation coefficient for the group with lesser maturity was lower. This supports the work of others who have investigated the relationship between ossification score and age within the USDA beef grading system  (Shackelford et al. , 1995 , Raines et al. , 2008)  and supports the idea that the plateau in ossification scores would reduce the slope of the association between ossification score and age. The strong correlation between these two measurements indicates that they are likely to have similar relationships with eating quality. This plateau in ossification score appeared in this dataset at about 8 years of age (3000 days).
Acknowledgements This research was supprted by  Meat and Livestock Australia and Murdoch University. Data were obtained through the financial contributions of the European research project  ProSafeBeef  (Contract No. FOOD-CT-2006-36241 ), the  Polish ProOptiBeef Farm  to Fork project funded by the  EU Innovative  ( POIG. 01. 03. 01-00-204/09 ), the  French 'Direction Générale de l'Alimentation' and FranceAgriMer , the  Irish Department of Agriculture Food  and  The Marine  under the  FIRM programme , and the  Northern Ireland Department of Agriculture and Rural Development 'Vision' programme. Furthermore , this project would not have been possible without the practical support of the  Association Institut du Charolais , the  Syndicat de Défense et du promotion de la Viande de Boeuf de Charolles  and the gourmet restaurants ' Jean Denaud ' and representatives of the beef industry across Europe.
All sequences were identified using the EzTaxon-e server  [35]  on the basis of 16S rRNA gene sequence data. Accession numbers of partial 16S rRNA gene sequences have been deposited in GenBank under accession numbers KM604797 to KM605107. All sequences were aligned using ClustalW version 2. 1  [36]. A maximum likelihood phylogenetic tree was constructed using RAxML version 7. 7. 2 [37]  with the GTRGAMMA model of DNA evolution. To obtain the statistical confidence of internal branches, 10000 bootstrapped trees were generated using RAxML. From this tree, unweighted UniFrac distances were computed  [38]  and unweighted pair group method with arithmetic mean (UPGMA) clustering was used to generate a dendrogram.
Metabolic fingerprint of the same 125 bacterial strains was performed using Biolog GN2 microplate according to the manufacturer's instructions. Microplates were inoculated with 150 μl of a bacterial suspension with A 595nm 0. 4 and incubated 24h at 25°C in dark condition. Then, color density of each well was measured at 595 nm using a iMark Microplate Absorbance Reader (Bio-Rad, Hercules, CA, USA). Data were normalized by dividing the raw difference value for each well by the average well color development (AWCD) of the plate, as suggested by Garland et al. [43]. Negative values were considered as 0 in subsequent data analyses. Shannon index was used to compute the diversity of carbon substrate utilization for each strain  [44].
Statistical analyses The ability of the 125 bacterial strains to grow on selective media and to use them, was analyzed using generalized linear models (glm) under a binomial distribution. Differences in the utilization of the 95 carbon substrates of the Biolog GN2 microplate among treatments (enrichment step x presence of the fungus) were evaluated by a Permutational Multivariate Analysis of Variance (PERMANOVA), using the adonis function from the vegan package in R with a Bray-Curtis dissimilarity matrix and 100000 permutations. Subsequently, the utilization of each substrate was estimated by a one-way ANOVA followed by the Tukey HSD post hoc test. All statistical analyses and graphics were computed using R software version 3. 0. 3 [45].
Comparison of different 16S rRNA gene data sets In order to compare results from cultivation and pyrosequencing methods, an operational taxonomic unit (OTU) based approach was performed using the Mothur software version 1. 29. 1  [46]. 16S rRNA gene pyrosequencing reads (accession numbers SRR900229 to SRR900250 in GenBank) were processed by largely following the Schloss standard operating procedure  [47] , as previously described  [25]. Briefly, sequencing errors were reduced by implementation of the AmpliconNoise algorithm and low-quality sequences were removed (minimum length 200 bp, allowing 1 mismatch to the barcode, 2 mismatches to the primer, and homopolymers no longer than 8 bp). Sequences were then trimmed to keep only high quality reads (Q 35). The 16S rRNA gene sequences of the 311 bacterial strains were also handled with Mothur.
The sequences of these two data sets were aligned in Mothur and classified against the SILVA bacterial SSU reference database v102  [48]. Chimera were removed using the chimera. uchime mothur command. Singletons were included in the analysis. Finally, all the sequences were assigned to family-level phylotypes using the naïve Bayesian classifier implemented in Mothur and clustered into OTUs using the average neighbor method. An OTU was defined at the 97% sequence similarity level. In order to compare our 16S rRNA gene sequences from the cultivated bacteria with other sequences from studies related to bacterial communities associated with decaying wood  [21, 24, 49, 50] , the RDP Library Compare tool version 2. 6 with 95% confidence threshold was used  [51]. The E0 sample, corresponding to the bacteria extracted from a forest soil, was excluded from this analysis.
The  UMR IaM  is supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE). Agence Nationale de la Recherche   ANR-09-BLAN-0012  http://www. agence-nationalerecherche. fr/. Funding was received by  VH EK EG PFK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.