dataesr/scientific-paragraphs-categorization huggingface dataset:
E. S. S. analyses so far. Note that the GNNA120 model including seasons, developed during this work, has been included in the HESS analysis software. Results show up to a 20% of difference and the following air shower evolution at high altitudes and up to 3% for the seasonal profiles in the important range for the gamma-ray induced air shower development. This emphasizes the importance of including seasonal changes and encourages us to further study effects at even shorter timescales. The possibility of including humidity effects into the density profiles has been studied. The effect never exceeds 1% in any of the studied cases. Despite this fact, it is a parameter that presents huge spatial and temporal fluctuations. Thus, we decided to not take it into account in the average profiles. Some changes with respect to the yearly slant depth profile have been found for autumn and spring.
Off-site readers also performed quantitative measures of signal intensity on up to 3 of the most representative lesions using ROI markers to determine SNR, contrast-to-noise ratio (CNR), and lesion percentage enhancement. ROIs were placed on unenhanced and enhanced T1 sequences. The 3 off-site readers were blinded to all patient data and to the time point of the MR imaging examination (first or second), diagnostic results of the imaging procedures, findings of any other readers (ie, investigators, on-site readers, other off-site readers), and the contrast agent administered. Readers were trained on an independent dataset of 15 MR imaging examinations to ensure a harmonized understanding of the reading criteria and were qualified after evaluation of inter-and intrareader variability on 15 new MR imaging examinations.
Statistical Methods Statistical analyses were performed by using SAS software (SAS Institute, Cary, North Carolina), Version 9. 2. Primary and secondary criteria were presented on the perprotocol set, including all randomized patients who had a valid off-site assessment of the primary criterion without major protocol deviation. The primary criterion, the noninferiority of gadoterate meglumine-enhanced MR imaging compared with gadobutrol-enhanced MR imaging for overall lesion visualization and characterization (dichotomized as good/excellent versus poor/fair), was assessed with a noninferiority margin set at Ϫ10%, which was considered clinically acceptable. If one assumed that approximately 85% of patients would have good or excellent lesion visualization and characterization (minimum score of 2) for each contrast agent and each independent reader, approximately 235 patients undergoing the 2 MR imaging examinations were needed to ensure that the difference between the 2 examinations did not exceed the noninferiority margin.
5 mL/s between the 2 contrast agent administrations (n ϭ 3), a difference of Ն10% between the 2 actual doses administered to a single patient (n ϭ 2), patients without detected tumoral lesions according to at least 2 off-site readers at both examinations (n ϭ 3), and loss of the second MR imaging data (n ϭ 1). Therefore, the per-protocol set used for analysis of evaluation criteria included 234 patients, 120 in the gadoterate meglumine/gadobutrol arm and 114 in the gadobutrol/ gadoterate meglumine arm. The 2 arms showed comparable demographic characteristics (On-line Table  2 ). Patients ranged in age from 18 to 98 years, with a mean of 53. 6 Ϯ 15. 1 years. There were more female than male patients (64. 2% versus 35. 8%). Body mass index ranged from 17. 4 to 51. 2 kg/m 2 , with a mean of 27.
ACKNOWLEDGMENTS The authors thank all participating investigators,  Pr K. J. Ahn ,  Dr A. Arbelaez ,  Dr E. Caro Osorio ,  Dr R. Carmody ,  Dr S. H. Cha ,  Dr J. W. Choi ,  Dr G. H. Chung ,  Dr A. H. Gil Valadez ,  Dr J. L. Jasso Rico ,  Dr J. F. Moreno ,  Dr F. Moser ,  Dr J. Nickerson ,  Dr J. A. Romero Figueroa ,  Dr H. Schwartz ,  Dr S. Seidman ,  Dr G. Shah ,  Dr P. Shah ,  Dr A. J. Schlesinger ,  Dr H. R. Vazquez Sanchez ,  Dr P. Woodard ; all study site coordinators; as well as  S. Rollin  and  L. Maho  for operational aspects. They also thank  B. Piednoir  for his statistical input and  A. -C. Laurent  for her support in editorial assistance. Disclosures:  Kenneth R. Maravilla-RELATED : Grant: Guerbet, Comments: grant for clinical trial study site*; Consulting Fee or Honorarium: Guerbet, Comments: consultant for study design and blinded image reviewer design; Support for Travel to Meetings for the Study or other Purposes: Guerbet, Comments: travel support for consultant meetings; UNRELATED:  Consultancy: Bracco ,  Guerbet.
Results RNA sequencing, transcriptome assembly and functional annotation. The cDNA library prepared from liver and kidney tissues of Lepus granatensis specimen "o" (Fig. 1 ), harvested in Navarra during the hunting season, was sequenced in 1/12 th of an Illumina HiSeq 2000 lane, produced a raw output of 14,645,969 100 bp paired-end reads. Adapter removal and quality trimming resulted in 13,052,770 paired-end reads for a total of 2,496,629,898 bp. Previously published data from 10 other L. granatensis specimens  22, 23  (Fig. 1  and Supplementary Table  S1 ; a total of 42,529,555 single-end 100 bp reads) were added to the dataset, which was used for de novo transcriptome assembly. A total of 54,838 contigs, ranging from 224 to 12,481 bp were obtained, which was reduced to 50,580 contigs after removing redundancy considering sequence similarity (Table  1 ).
The transcriptome was further filtered by retaining only contigs with reciprocal best blast hit annotation against the rabbit genome (21,833 contigs) and/or containing a predicted open reading frame (ORF), resulting in a final assembly with 24,608 contigs, an N50 of 1,724 bp and a total length of 26,161,714 bp (Table  1 ). When compared with the available transcriptome produced by Cahais et al. 22  , the addition of relevant paired-end data caused important   S1  for a detailed description); numbers indicate sample size, pie charts the proportion of STRUCTURE assignment to each of 3 clusters using the LOC prior and the complete SNP dataset (100 loci); the "X" marks the inferred origin of the range expansion; grey letters indicate specimens for which RNA was sequenced and used to build the transcriptome ("d-n" from Cahais et al.
22  and Gayral et al. 23  , and "o" from this work). (b) Localities sampled for L. timidus, indicating the sample sizes; two sampling localities are not shown on this map (RUS -Russia; and FER -Far East Russia). (c) Proportion of the mitochondrial DNA lineages, native L. granatensis or introgressed from L. timidus, in the genotyped samples. Maps were generated in vectorial format using Inkscape v0. 91 (https://inkscape. org). improvements in several statistics of the reconstructed assembly, such as unfiltered N50 (1334 vs. 909) and reference coverage (32% vs. 23%) (Supplementary Table  S2 ). To predict the potential functions of the assembled transcripts, the retained unigenes were aligned with various protein databases in addition to the O. cuniculus protein collection: NCBI, SwissProt, Gene Ontology, InterProScan and KEGG.
Furthermore, we estimated the differentiation between mtDNA introgressed and non-introgressed populations on our much larger sample in an analysis of molecular variance (using an AMOVA  25  ). We found no significant difference in the distribution of the Φ CT statistic between category A and either category B or C SNPs (Mann-Whitney test on pairs and Kruskal-Wallis on all three; mean Φ CT of 0. 01623, 0. 0149 and 0. 0155 for classes A, B and C respectively). Therefore, the results based on the 11 RNA-sequenced samples were very poor predictors of the genetic structure of the species, and no ascertainment bias towards SNPs with the targeted differentiation pattern seemed to prevail in our SNP dataset. Using BayeScan  26  , we inspected whether some of our loci showed any outlier pattern of differentiation among sampling localities.
We thus applied the test of expansion species-wide, as well as separately on subsets of populations (regions) grouped according to the cluster they were assigned to in majority (region R1 in the Southwest where K1 was predominant, R2 in the Centre and Northeast where K2 was the most frequent, and R3 in the Northwest where K3 was the major cluster, see Fig. 1a ). Taking all three regions together, a signal of range expansion was inferred, with an origin near locality ALT (Fig. 1a , P < 0. 05) (see detailed results for all analyzed datasets in Supplementary Table  S5 ). Allowing for the possibility of multiple origins, significant support for range expansion was only obtained when considering R1 and R2 together with origin again near ALT (P < 0. 001), and when taking R2 alone, with origin near CRE (P < 0.
Interest in this question is revived by recently reported suspicion of interspecific cytonuclear incompatibilities in a mammal species  30. Given the lack of association between mtDNA and our nuclear SNPs, we were able to consider the chosen loci as neutral to make inferences on population history and demography. We obtained similar results when using both the randomly selected loci and the dataset removing potential F ST outlier loci (that could actually result from the range expansion we also inferred  27  ), which suggests that the complete SNP set is appropriate for this purpose. The relative demography of interacting species is an important determinant of the rate and direction of introgression between them  8. Importantly, it can explain unequal levels of introgression along the genome and geographic gradients of introgression, similar to patterns resulting from introgression promoted by natural selection along an environmental gradient.
The library was then pooled in equimolar proportions with cDNA libraries produced for other purposes and sequenced in 1/12 th of one lane of an HiSeq2000 at the QB3 Computational Genomics Resource Laboratory (CGRL), University of California, Berkeley, producing 100 bp paired-end reads. Low quality reads were removed using CASAVA-1. 8 FASTQ Filter, adapter sequences were identified and removed using Cutadapt v1. 3  31  and reads were trimmed for quality using Trimmomatic v0. 32  32  with options LEADING:5 TRAILING:5 SLIDINGWINDOW:4:15 MINLEN:20. Previously published transcriptome single-end sequence reads from 10 L. granatensis specimens  22, 23  (data available from ftp://ngsisem. mbb. univ-montp2. fr/phylogenie_et_evolution_moleculaire/pub/popphyl/reads/Lepus_granatensis/) were added to the dataset (see geographic distribution of the 11 sampled specimens in Fig.
1a ). De novo transcriptome assembly was constructed using Trinity v. 2. 1. 0  33 with default parameters and data from the 11 specimens (single-end data from 10 specimens and paired-end data from 1 specimen). Large amounts of chimeras and poorly supported contigs can arise during the assembly process. Therefore, in order to evaluate the quality of the produced assembly, sequenced reads were mapped back to the reference assembly and quantitative and qualitative measures were applied using TransRate  34. A redundancy filter was then applied using CD-HIT-EST v 4. 6. 4  35 to remove contigs with more than 95% similarity. To evaluate the transcriptome completeness, cDNA and peptide collections of Oryctolagus cuniculus (European rabbit) were downloaded from ENSEMBL (http://www. ensembl. org/info/data/ftp; release 2.
Unigenes were additionally annotated using the blastx algorithm against Swiss-Prot 39 and NCBI non-redundant protein databases  40  applying an e-value cut-off of 1e-5. Additionally, NCBI non-redundant annotation output was incorporated on Blast2GO 41 in order to perform a functional classification of the transcripts, through the assigning of Gene Ontology (GO) terms and prediction of metabolic pathways using the Kyoto Encyclopedia of Genes and Genome (KEGG)  42, 43. Complementarily, InterProScan  44  was used to identify protein domains and the output was quantified. Inference of single nucleotide polymorphisms and genotyping. Mapping of the paired-end and single-end reads of the 11 L. granatensis specimens onto the de novo assembled transcriptome was performed using the bwa-mem v0. 7. 12 algorithm with default parameters  45  , and the resulting alignments were sorted using SAMtools v0.
One first class of SNPs, selected randomly among independent contigs was identified for further genotypingclass A. Two additional classes were built, favoring SNPs with higher F ST between regions with and without mtDNA introgression considering the 11 sequenced specimens. Among these SNPs, those laying on genes with functions related with the mitochondrion were ascribed to class B. These are candidates for potential coevolution or cointrogression with mtDNA. A third class was then considered among SNPs on genes with no mitochondria functions -class C -which served as control for SNPs from class B. Genes with functions in the mitochondria (mitonuc genes) were defined according to the InterMitoBase database list, based on human information  49  (708 genes). Given that human gene annotation is extensively more complete than the rabbit one, gene codes were obtained using the human-rabbit 1:1 orthologs obtained from Biomart, or using blastx alignment against the human collection of peptides, when orthology information was missing.
Genotype data analyses. Population genetics analyses were performed using 314 genotyped L. granatensis specimens that were organized in 20 sampling localities with at least 12 individuals each. Each genotyped locus was checked for conformation to Hardy-Weinberg proportions using genepop v4. 2  50. The same software was used to test for linkage disequilibrium between pairs of loci. Given that the selections of SNPs from classes B and C were ascertained taking into account the geographic distribution of mtDNA introgression from L. timidus, we tested whether we could find any correlation using Bayenv2  24  and treating introgression frequencies as an environmental variable. The method initially estimates a null covariance matrix from a set of randomly selected loci, and then assigns Bayes Factors to each SNP that measures whether allele frequencies co-vary with an environmental variable, over the null model of population structure.
The set of randomly selected loci (class A) was used to estimate the covariance matrix based on 10 million iterations. Three replicate runs were performed to ensure the consistency of the estimates, and Bayes factors were averaged over the independent runs. The variable was normalized following the recommendation. A Bayes Factor > 3 was considered as indicative of correlation between the allele frequencies at a given locus and the variable. We then checked whether the ascertainment based on differentiation between regions with and without mtDNA introgression based on the 11 sequenced specimens, used to select SNP classes B and C, indeed produced an enrichment of differentiation. This was tested using a locus-by-locus AMOVA  25  , as implemented in Arlequin v3. 5  51. Also, we tested whether F ST outliers could be found in our dataset, using the Bayesian method implemented in BayeScan v2.
116. This method estimates whether subpopulation-specific allele frequencies differ from the common gene pool, as measured by an F ST coefficient. The F ST coefficient is decomposed in a locus-specific parameter, alpha, and a population-specific parameter, beta. A false discovery rate of 0. 05 was used. Note however that this method is expected to produce many false positives when the true underlying demographic model is range expansion  27. Analyses of population structure were further performed using the randomly selected loci, the dataset without F ST outliers and the full dataset. To investigate the partition of genetic diversity in Lepus granatensis the admixture model implemented in STRUCTURE 2. 3. 4  28  was applied. A variable number of K populations, from 2 to 10, was considered and three replicate runs per partition with 1 million steps after a burn-in period of 500 000 were performed.
The model considering sampling locations as prior information (LOC prior) was applied because it is expected to better detect shallow structure. Replicate runs were analyzed using CLUMPP v1. 1. 2  52 and DISTRUCT v1. 1  53  was used to plot the results. The best number of populations, K, was inferred using Evanno's delta K method  54  , as implemented in STRUCTURE HARVESTER  55. Possible partitions of genetic diversity in the dataset were further investigated using principal components analyses, as implemented in Eigenstrat  56. In addition, the existence of a correlation between population differentiation and geographic distance was verified using the ISOLDE method implemented in Genepop  50. Finally, evidence for a range expansion and its putative origin was tested using the R library rangeExpansion  29.
Acknowledgements This work was developed in the frame of project  HybridAdapt  ( FCT-ANR/BIA-EVF/0250/2012 ), supported by  Portuguese National Funds  through the  Fundação para a Ciência e a Tecnologia, FCT. J. M. -F. was supported by  POPH-QREN  funds from the  European Social Fund  and  FCT (IF/00033/2014 FCT Investigator grant ). SNP genotyping was funded by grant  PTDC/BIA-EVF/111931/2009 , supported by  FEDER  funds through the  COMPETE program  and Portuguese national funds through  FCT. Laboratory work at the University of Montana  was performed in the  UM Genomics Core , supported with a grant from the  M. J. Murdock Charitable Trust. Transcriptome sequencing was performed through the  Vincent J. Coates Genomics Sequencing Laboratory at University of California Berkeley , supported by  NIH   S10 Instrumentation Grants   S10RR029668  and  S10RR027303.
Additional instrumentation, laboratory, and computational support was provided by  CIBIO NEW-GEN  sequencing platform, supported by  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement no. 286431. Support was also obtained from project " Genomics and Evolutionary Biology ", co-funded by North Portugal Regional Operational Programme 2007/2013 -ON. 2 -O Novo Norte -under the National Strategic Reference Framework and the  European Regional Development Fund. We thank  Michael Hofreiter  and two anonymous reviewers for helpful comments on the manuscript. This research was conducted in the scope of the  International Associated Laboratory (LIA) "Biodiversity and Evolution " supported by  InEE  ( Institut Ecologie et Environnement ,  CNRS, France)  and  FCT (Portugal).
The association of p27 to their respective binding sites of target genes was performed by qPCR using the primers listed in S1 Table. ChIP data were quantified using the comparative delta Ct method  [24]. Chip data from wt cells were calculated independently of the data from p27 -/-cells. Specifically, for all chromatin immunoprecipitations, IP and control (no antibody) values were normalized by their input and fold changes relative to control were determined using the ΔΔCt method; a mean fold change (2 -ΔΔCtAVE ) value along with a s. e. m. (abs(((2 -ΔΔCtAVE x 2 -ΔΔCtSEM )-(2 -ΔΔCtAVE / 2 -ΔΔCtSEM )) / 2)) value were determined.
ChIP-seq data analysis Illumina pipeline analyzed short reads were uniquely aligned allowing at best two mismatches to the UCSC (The Genome Sequencing Consortium) mouse genome version mm9, using the program BOWTIE  [25]. Peak caller algorithm MACS (version 1. 4) was used to determine enriched peak region with parameters:-nomodel,-tsize = 46,-bw = 300. Shift size were determined using Pyicos  [26]  strand correlation method. Enriched peaks were annotated to nearest EnsEMBL  [27]  gene (EnsEMBL Biomart version 54) using Bioconductor package ChIPpeakAnno  [28]. Distribution of enriched reads along the genomes and transcription start site (TSS) of RefSeq genes were determined using CEAS  [29]. ChIP-seq data are available in the ArrayExpress database (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-5105.
Functional enrichment analysis Functional annotation of target genes is based on Gene Ontology (GO)  [34]  Consortium, 2000; (http://www. geneontology. org) as extracted from EnsEMBL  [27]. Accordingly, all genes are classified into the ontology category biological process. Enrichment analysis was performed using the public DAVID tools (Database for Annotation, visualization and integrated discovery v 6. 7) (http://david. abcc. ncifcrf. gov) [35].
Cell adhesion analysis To examine the behavior of p27 deficient cells in terms of adhesion we used the Roche xCELLigence real-time cell analysis system. This system measures the area that is occupied by the cell on real-time which represents itself as impedance. During the time the cell attaches on the culture plate the area occupied increases and this causes an impedance of the current. Cell-index values reflecting impedance changes are automatically and continuously recorded by the RTCA DP instrument. wt and p27 -/-MEFs were trypsinized and the same number of cells were plated on xCELLigence plates. Normalized cell index data obtained few hours after cell plating are plotted over time axis. Slope of this graph is interpreted as substrate adhesion capacity of the cells.
GO analysis of protein-coding genes The 852 protein coding genes that contained a p27 peak at its vicinity (S2 Fig) , were subjected to Gene Ontology (GO) analysis using the DAVID Bioinformatics program  [35]. The most enriched GO terms (Biological process) included: intracellular signaling cascade (52 genes), ion transport (39 genes), cell adhesion (32 genes), neuron differentiation (28 genes), positive regulation of transcription (27 genes), cell morphogenesis (25 genes), negative regulation of transcription (24 genes), behavior (24 genes), cell motion (24 genes), immune system development (20 genes), hematopoiesis (17 genes), regulation of cell communication (16 genes), axon guidance (12 genes), muscle cell differentiation (11 genes) and cell cycle (7 genes) (Fig  5 ). Expression microarray analysis showed that 4022 genes were differentially expressed in p27 -/-MEFs  [16].
In MEFs, we have detected 1417 p27-BSs being 852 of them annotated to protein coding genes. On comparing these 852 genes with those differentially expressed in p27 -/-MEFs we observed that only 126 of them were expressed in quiescent p27 -/-cells (Fig 6A ), being 62 down-regulated and 64 up-regulated. Using the DAVID program we also looked for the GO terms enriched in these 126 p27-TGs. Proteolysis, phosphorylation, cell morphogenesis, behavior, cell motion, negative regulation of transcription, cell cycle, anion transport and muscle cell development were the GO terms mostly enriched in this group of genes (Fig  6B ). Most of these terms were also observed in the biological processes enriched in the p27 ChIP-seq (Fig  5 ). Altogether, these results indicate that in quiescent p27 -/-cells only a relatively reduced number of the putative p27-TGs (126 genes out of 852) (~15%) were differentially expressed, despite the total number of differentially expressed genes was high.
These results suggest that the role of most of the putative p27-TGs might be relevant in non-quiescent cells, for instance in proliferating cells, or in other cellular types. The evidence that we have been not able to detect putative p27-BSs in the proximity of most of the genes differentially expressed in quiescent p27 -/-cells  [16]  suggested that p27 could indirectly regulate the expression of most of these genes by controlling the expression of TFs. To explore this possibility, TFCONES database  [37]  was used to identify genes encoding TFs among these 126 p27-TGs. Results revealed that 12 genes containing p27-BSs and differentially expressed in p27 -/-cells encode TFs (Fig  6C ). To demonstrate that actually p27 regulates the expression of these TFs trough the annotated p27-BSs, we performed luciferase assays using a luciferase reporter gene including these specific p27-BSs.