dataesr/scientific-paragraphs-categorization huggingface dataset:
These results provide the first evidence for discovering how and which TE families represent a major force in shaping the canine lncRNA expression and their tissue-specificity expression patterns in a lineage-specific fashion. Despite the number of tissues investigated in this study (n = 26), tissue-specificity is a limiting factor in annotating the whole repertoire of lncRNAs of a species, since their annotation depends on the availability of  numerous tissue and cell types as well as tissue subtypes and multiple spatio-temporal conditions. Additional tissues and subtypes and development stages must be continuously collected through large databases as we do with the Cani-DNA French database (https://dog-genetics. genouest. org/en/brc-cani-dna/) in order to provide additional anatomical subtype data for identifying an extended catalogue of lncRNAs and their isoforms.
Materials and Methods Datasets. The 26 RNA-seq sample dataset (Supplementary Table  S1 ) represents a wide variety of canine celltypes and tissue-types. The 26 tissues can be classified by Anatomical System as inspired by the Expression Atlas database (https://www. ebi. ac. uk/gxa/home/), as shown in the Supplementary Table  S1. Two tissues correspond to 'Digestive system' , 6 tissues to 'Integumental system' , 2 tissues to 'Musculoskeletal system' and 5 tissues to 'Nervous system'. The others tissues correspond to 'Unmapped' and 'Testis'. RNA extraction and directional sequencing were performed as described in Wucher et al. 9  and Hoeppner et al. 8  and are available through accession numbers SRP077559, SRX111061 -SRX111071, and SRX146606 -SRX146608. The canine 'canFam3. 1-plus' annotation  9  (containing 10,444 lncRNA and 21,810 protein-coding genes (mRNAs)) was used as the reference annotation for this study and the canFam3 assembly version was the reference genome.
Quantification of mRNAs and lncRNAs expressions. Based on the bio-informatic protocol described in Djebali et al. 24  , FASTQ reads were aligned on the canine reference genome and transcriptome using the STAR  64  program (version 2. 5. 0a) and we determined the gene and isoform expression levels for both lncRNAs and mRNAs for each of the 26 RNA-seq with RSEM  26 (version1. 2. 25). Finally, the RSEM output files were parsed in order to extract gene expression level, normalised in Transcripts Per Million (TPM) and merged to obtain one matrix expression file with gene names in rows and tissue expression levels (TPMs) in columns. Characterisation of lncRNA expression. Tissue-specificity. To calculate the tissue-specificity score 'tau'  28  , ranging from 0 to 1 (with tau = 1 for highly tissue-specific genes and tau = 0 for broadly expressed genes), we used the matrix file described above and calculated the following equation  28  : ^τ = ∑ - - = = ≤ ≤ i x (1 X ) n 1 ; X X max ( ) i 1 n i i 1 i n i where n corresponds to the number of tissues analysed and x i is the gene expression in the tissue i.
The specificity score was calculated using a threshold of 1 TPM (to be stringent). To extract a stringent set of tissue-specific genes, we used a tau threshold greater than 0. 95 corresponding to a ratio of 4 between the first and the second tissue (e. g. max(x i )/max 2nd (x i ) > 4) or, in others words, corresponding to cases where gene i is four times more expressed in the first tissue compared to the second most highly expressed tissue. Note that we provide a perl script dedicated to computing the 'tau' score on GitHub available here: https://github. com/tderrien/IGDR/blob/master/script/specificity_score. pl. Finally, the 'tau score' together with eight others metrics dedicated to computing tissue-specificity were recently benchmarked in Kryuchkova-Mostacci et al. 29  , and the authors concluded that "the best overall method to measure expression specificity appears to be Tau" because it consistently appears to be the most robust method for retrieving tissue-specific genes.
Heatmap and clustering analysis. For each biotype (lncRNAs and mRNAs), we computed all pair-wise Spearman correlations between the 26 tissues in order to obtain a matrix of distance. Using this file, together with a meta-data file containing anatomical system classification (e. g. nervous system, integumental system, musculoskeletal system, digestive system, testis, and unmapped), the heatmap and the associated hierarchical ascendant clustering (with default parameters: euclidean distance and method = 'complete') were created as in Breschi et al. 48 , using R software (version 3. 2). TE content. Canine TE annotations were based on the RepeatMasker 36 database, downloaded from the UCSC table browser  65  (September 2017). Four major classes were analysed: DNA transposons and retrotransposons, LTRs, LINEs and SINEs; each of them being divided into families (e.
TE content and gene expression. The mixOmics tool 37 (version 6. 3. 1) was used to compute canonical correlations to highlight potential correlations between two matrices X and Y, where X is the matrix of lncRNA (or mRNA) expressions in the 26 tissues and Y is the matrix of TE content in lncRNAs (or mRNAs) exons and promoters. The rcc function of the mixOmics package (with method = 'ridge' and ncomp = 3) was used to seek for linear combinations of the variables (canonical variables) to reduce the dimension of the datasets. The heatmap was constructed with the mixOmics function cim (Clustered Image Maps) with default values, except ncomp = 3.
Comparative genomics and transcriptomics. Comparative genomics. To identify conserved canine lncRNAs and mRNAs in humans, we used the Ensembl Compara database  39. Briefly, the program annotates orthologous regions between two or more species based on Whole Genome Alignments (WGA) computed using the EPO (Enredo-Pecan-Ortheus) pipeline  39. All canine gene coordinates were then mapped onto the human genome (version: GRCh37 using version 75, GENCODE version 19), having the same biotype (coding or non-coding) and considering only the one-to-one (1:1) relationships. GERP score. We used the EnsEMBL Compara API to retrieve GERP scores  46  of the dog genome assembly (can-Fam3) (one wig file per each dog chromosome) computed on the 53 eutheria mammals. To get a single value per lncRNA and mRNA gene, we projected all exons of each gene onto the dog genome and calculated the median GERP score for each gene.
Comparative transcriptomics with human ENCODE data. We extracted the human gene expression using data produced by the ENCODE project  49  (https://www. encodeproject. org/matrix/?type=Experiment&replicates. library. biosample. donor. organism. scientific_name=Homo+sapiens&biosample_type=tissue&assay_title=-total+RNA-seq&assembly=hg19&award. project=ENCODE). We only selected data where tissues between humans and dogs could be matched. We found 11 dog:human matching tissues: adrenal gland, gut-colon, heart, liver, lung, muscle, ovary, pancreas, skin, spleen and testis. From the available RSEM files, we extracted the expression levels at the gene transcript levels in TPM for the dog data. When multiple RSEM files for a tissue replicates, we averaged the expression level per gene. We then determined the statistical correlation, with the Spearman correlation test, of the level and the distribution of the expression between these 11 human and dog tissues.
5 and p. adjust < 0. 05. GO term analysis. From the 5,711 canine mRNAs that were significantly co-expressed with their lncRNAs (n = 3,410), we extracted human, orthologous EnsEMBL gene IDs of mRNAs (n = 3,977). To identify gene ontology (GO) terms significantly enriched in this list of gene IDs, we used the web-based GEne SeT AnaLysis Toolkit (WebGestalt 66 ) software with the following settings: Method of Interest = 'Overrepresentation Enrichment Analysis' (ORA) and Functional Database class = geneontology (GO) named 'Biological_Process_noRedundant'. Finally, we selected the Gene ID type 'ensemble_gene_id' , to upload our orthologous data using ENSG IDs and selected genome for Reference Set for Enrichment Analysis. We can add ' Advanced parameters' , so we keep default parameters (Minimum Number of Genes for a Category: 5; Maximum Number of Genes for a Category: 2000 and Multiple Test Adjustment: BH) and use the Significant Level: FDR 0.
Acknowledgements The biological samples were obtained from the ' Cani-DNA_CRB ' , which is part of the  CRB-Anim infrastructure  (http://dog-genetics. genouest. org). We would like to thank the following funding agencies: the ' Cani-DNA_CRB ' [ ANR-11-INBS-0003 ], the  CNRS  ( Centre National de la Recherche Scientifique ), the  Université Rennes1  and the  Brittany Region (France)  (PhD funding for CLB). We thank the  LUPA consortium  (http://eurolupa. org)and the  BROAD Institute  for the sequencing. We thank  Sarah Djebali  of the  INRA GenPhyse of Toulouse  and members of the canine genetics team at  IGDR-Rennes. We thank  Ignacio Gonzalez  for help with the analysis using mixOmics. We also thank the  Genouest Bioinformatic platform, Rennes, France  (https://www. genouest. org/)for providing the necessary computing resources.
Secondly, we estimated psychometric parameters (i. e. , reliability and validity) from empirical and simulated dataset with WISC-IV. The difference in the estimation of psychometric qualities of each IQ SF from the simulated data is very close to those derived from empirical data. We thus selected the three best IQ SF based on these psychometrics parameters estimated from simulated datasets. For each selected short form of the WISC-IV, we estimated the screening quality in our sample of IGC. Thirdly, we created IQ SF with 2-and 4-subtests from the WISC-V (Wechsler, 2016) with simulated dataset. We then highlighted the three best short forms of WISC-V based on the estimated psychometric parameters. The results are interpreted in terms of validity, reliability and screening quality of IGC. In spite of the important changes in the WISC-V, our findings show that the 2-subtest form, Similitaries + Matrix Reasoning, and 4-subtest form, Similitaries + Vocabulary + Matrix Reasoning + Block Design, are the most efficient to identify the IGC at the two recent versions of Wechsler scales.
Participants The data was collected from the WISC-IV produced by 117 French IGC (mean age: 10. 39, SD: 1. 03, 74% boys) and 52 French intellectually typical children (mean age: 10. 57, SD: 2. 66, 79% boys). Clinician psychologists gave us the data completely anonymous and in the respect of French deontological code. To preserve the anonymity of the children, we chose not to contact the parents and their children. The groups did not significantly differ in terms of age, t (57. 902) = -0. 485, p = 0. 629, d = -0. 109, or gender, χ 2 (1) = 0. 550, p = 0. 458. The children were contacted through teachers, school psychologists or licensed psychologists. In order to allow for measurement errors, the inclusion criterion for intellectually gifted group was set to FSIQ or GAI score greater than or equal to 125 in accordance with the current recommendation  (McIntosh et al.
Procedure All of the statistical analyses were made using version 3. 4. 2 of R software  (R Core Team, 2017). We estimated the interclass correlation of each IQ SF and the full-scale using the irr library  (Gamer et al. , 2012)  and the hierarchical omega (ω H ) of each IQ SF using the MBESS library  (Kelley, 2018). We also computed the 95% confidence intervals for the correlation comparisons  (Zou, 2007)  using the cocor library  (Diedenhofen and Musch, 2015). The data relevant to the preparation of short forms was extracted from fully administered WISC-IV protocols. The FSIQ scores were computed from 10 core subtests from the WISC-IV. The GAI scores were obtained from the VCI and PRI  (Lecerf et al. , 2010a). The current study is composed of three steps in our statistical analyses. Firstly, the scores from different IQ SF were computed using the linear equation method  (Tellegen and Briggs, 1967).
They were divided by the sum of the standard scores given by the VCI (Vocabulary [Vo], Similarities  [Si]  and Comprehension  [Co] ) and PRI (Block Design [Bd], Picture Concepts [Pc], and Matrix Reasoning  [Mr] ). Secondly, we realized a simulation of 1,000,000 data with the Choleski decomposition from correlation matrix of WISC-IV (Table  4. 1; Wechsler, 2005) using the mvtnorm library  (Genz et al. , 2018)  (see  Giofrè et al. , 2017  for a similar approach). We realized a comparison between psychometrics parameters (i. e. , reliability and validity) estimated from empirical and simulated datasets. Then, we selected the three best IQ SF based on these psychometrics parameters estimated from simulated dataset. For each selected short form WISC-IV, we computed the indicators of a receiver operating characteristic (ROC) such as the sensitivity, the sensibility, the false positive rate (FPR), the false negative rate (FNR), and the Area Under Curve (AUC) from our empirical sample using the pROC library  (Robin et al.
, 2011). The AUC is used as a general measure to estimate the performance of the IGC classification  (Fawcett, 2006). Thirdly, we realized another simulated dataset from correlation matrix of WISC-V (Table  4. 1;   Wechsler, 2016). From this simulated dataset, we created the different short forms of WISC-V. We then highlighted the three best short forms of WISC-V based on the estimated psychometrics parameters from simulated dataset. The data and R script used for the simulations and the statistical analyses are available on the Open Science Framework (OSF) at http://osf. io/dax8p.
Description of the Sample All of the participants obtained a GAI score equal to, or greater than 125. The descriptive data from our sample, as well as the mean and standard deviation of the FSIQ, the GAI, and the substests from WISC-IV, are shown in Table  1. The deviations and equations for each IQ SF are presented in Table  2. All of the scores from each 2-subtest and 4-subtest IQ SF have skewness and kurtosis coefficient lying between -1 and 1. All of the 2-and 4-subtests forms have an average value greater than 125 in intellectually gifted children and an average value of 107 in typical children. The raw data is available in the Supplementary Material (Data Sheet 1).
Short Form WISC-IV Estimation With Simulated Dataset We realized a comparison between the reliability and validity indicators from real and simulated data in WISC-IV. The composite indicator Rc calculated with our sample and the simulated dataset are highly correlated [r (36) = 0. 988]. In addition, there are few differences between each indicator of reliability and validity estimated with our sample and these with the simulated dataset have few differences (Table  3 ). The near perfect duplication of psychometric quality indicators shows that their estimation with simulated data is very close to those derived from real data. We thus selected the short forms from the simulated dataset. The 2-subtest and 4-subtest forms are ranked by decreasing value of the composite score R c , determined to use the reliability index and each validity index (see Table  4 ).
Identification Efficiency of Short-Form WISC-IV From Empirical Data After selecting the three best short forms with 2-and 4-subtests from simulated dataset, we realized the AUC, sensitivity and sensibility of the three best IQ SF from empirical dataset. In Table  5 , the short forms with 2 subtests and 4 subtests are ranked in descending order of the AUC indicating the predictive performance in identifying the IGC. All short form scales have a high AUCs suggesting a highly predictive performance with 2and 4-subtests models. The difference among all models is low ( AUCs < 0. 05). Among the forms with 2 subtests, the form with the best performance is still Similitaries + Matrix Reasoning  [SiMr]. They allow more than 74% of IGC in our sample to be correctly identified. The SiMr form has the probability of around 1.
Short-Form WISC-V Estimation From Simulated Dataset The 2-subtest and 4-subtest forms are ranked by decreasing value of the composite score R c (see Table  6 ). All of the IQ SF scores produced by the 2 subtests are significantly correlated with the FSIQ, r ′ (1e6) ∈ [0. 741; 0. 833]; p < 0. 01. All of the IQ SF scores computed from 4 subtests are also correlated with the FSIQ, r ′ (1e6) ∈ [0. 921; 0. 940]; p < 0. 01. Like at the WISC-IV, the short form with the Similarities + Matrix Reasoning [SiMr] subtests has the highest agreement score of all 2 subtest forms, R c = 0. 849. It is strongly correlated with the FSIQ, r ′ (1e6) = 0. 833; p < 0. 01, and GAI score, r ′ (1e6) = 0. 855; p < 0. 01. In 87% of cases, the IQ SF score for the SiMr form correctly identifies the IGC in our sample, and their IQ SF lie within 2 standard errors of measurement of the FSIQ.
CONCLUSION The identification of IGC based on the use of standardized tests such as the Wechsler scale is often time-consuming. This drawback can prevent other evaluations from being made, which could be essential for the education of these children with specific needs. The development of a short form of the recent version of Wechsler scale is thus useful for the fast and efficient identification of IGC. This short form would then allow the need for a more in-depth evaluation of various cognitive and socioemotional characteristics to be determined. Our study evaluated nine 2-subtest forms and nine 4-subtest forms, based on the linear method of  Tellegen and Briggs (1967). In order to evaluate these different short forms, we computed with simulated datasets several psychometric indicators that were reorganized into groups with an agreement indicator for FSIQ and GAI scores.
ACKNOWLEDGMENTS First and foremost, we extend our thanks to the children and families who participated in our study. The authors also extend their warm thanks to  Claire Touchet ,  Corentin Gonthier ,  Émilie Lacot ,  Geoffrey Blondelle ,  Yannick Gounden , and  Marc Chatterji  for their advice and their proofreading of this paper. The authors thank also  Académie de Versailles ,  Florence Pâris ,  Philippe Coche ,  Isabelle Sage , and  Éric Turon-Lagot  for their help. The authors also want to thank the two reviewers for their suggestions to improve this article. This research was supported by the  European Regional Development Fund (ERDF)  and the  Regional Council of Haut de France  for the  HPISCOL  project: Enfants et adolescents à haut potentiel: Identification et scolarisation.
This paper describes the observations and the first data release (DR1) of the ESO public spectroscopic survey "VANDELS, a deep VIMOS survey of the CANDELS CDFS and UDS fields". VANDELS' main targets are star-forming galaxies at redshift 2. 4 < z < 5. 5, an epoch when the Universe was less than 20% of its current age, and massive passive galaxies in the range 1 < z < 2. 5. By adopting a strategy of ultra-long exposure times, ranging from a minimum of 20 hours to a maximum of 80 hours per source, VANDELS is specifically designed to be the deepest ever spectroscopic survey of the high-redshift Universe. Exploiting the red sensitivity of the refurbished VIMOS spectrograph, the survey is obtaining ultra-deep optical spectroscopy covering the wavelength range 4800-10000 Å with sufficient signal-to-noise to investigate the astrophysics of high-redshift galaxy evolution via detailed absorption line studies of well defined samples of high-redshift galaxies.
The VANDELS-DR1 is the release of all medium-resolution spectroscopic data obtained during the first season of observations, on a 0. 2 square degree area centered around the CANDELS-CDFS and CANDELS-UDS areas. It includes data for all galaxies for which the total (or half of the total) scheduled integration time was completed. The data release contains 879 individual objects, approximately half in each of the two fields, which have a measured redshift, with the highest reliable redshifts reaching z spec ∼ 6. In the data release we include fully wavelength and flux-calibrated 1D spectra, the associated error spectrum and sky spectrum and the associated wavelength-calibrated 2D spectra. We also provide a catalogue with the essential galaxy parameters, including spectroscopic redshifts and redshift quality flags measured by the collaboration.
In this paper we present the survey layout and observations, the data reduction and redshift measurement procedure and the general properties of the VANDELS-DR1 sample. In particular we discuss the spectroscopic redshift distribution, the accuracy of the photometric redshifts for each individual target category and we provide some examples of data products for the various target types and the different quality flags. All VANDELS-DR1 data are publicly available and can be retrieved from the ESO archive. Two further data releases are foreseen in the next two years with a final data release currently scheduled for June 2020 which will include improved re-reduction of the entire spectroscopic data set.
0 < z < 7. 0, including star forming galaxies at redshift z > 2. 4, an epoch when the Universe was less than 20% of its current age and massive passive galaxies in the range 1 < z < 2. 5. VANDELS has observed in the wavelength range 4800-10000 Å with intermediate resolution. VANDELS' prime motivation is to move beyond simple redshift acquisition, by obtaining spectra with high enough signal-to-noise to allow detailed absorption and emission line studies in individual spectra, derive metallicities and velocity offsets, and finally derive improved constraints on physical parameters, such as stellar mass and star formation rates. These informations will enable a detailed investigation of the physics of galaxies in the early Universe. By targeting two extragalactic survey fields with superb multi-wavelength imaging data, including the best optical+nearIR+Spitzer imaging, VANDELS will produce a unique legacy dataset for exploring the physics underpinning high-redshift galaxy evolution.
In this paper, after a brief summary of the target selection (Section 2), we present the survey layout and observations (Section 3), the data reduction process (Section 4), the redshift measurement procedure (Section 5) and, finally, the content of the first data release (DR1) (Section 6) with the general description of the data publicy available, the properties of the galaxies in the release and some examples of data products for different types of galaxies. In a companion paper  (McLure et al. 2018 ), we present complementary information including the main scientific motivations of the survey, the assembly of the photometric catalogs, the determination of the photometric redshifts and the selection for the different category of targets that were observed in the survey. We refer to total magnitudes in the AB system  (Oke & Gunn 1983).
Observations All VANDELS observations were obtained in visitor mode during a period which ran from August 2015 to February 2018. Spectra belonging to the Data Release 1 were observed during the first observational season which ran from August 2015 to February 2016. The individual OBs were designed to deliver a total of one-hour of on-source integration time. Each OB consisted of three integrations of 1200s, obtained in a three-point dither pattern, with dither offsets of 0. 82 arcseconds (dither positions 0,-0. 82",+0. 82") corresponding to 4 pixels. This was done to remove most of the small-scale detector pattern and facilitate sky-subtraction. For the same reason we have tried to obtain an equal number of frames at each of the three positions, even if on individual nights it was not always possible to complete the OBs.