dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors acknowledge the financial support of the funding agencies:  Centre National de la Recherche Scientifique (CNRS) ,  Commissariat  à l'énergie atomique et aux énergies alternatives (CEA),  Commission Européenne (FEDER fund and Marie Curie Program) ,  Institut Universitaire de France (IUF) ,  IdEx program  and  UnivEarthS Labex program at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ),  Région Île-de-France (DIM-ACAV) ,  Région Alsace  (contrat CPER),  Région Provence-Alpes-Côte d'Azur, Département du Var and Ville de La Seyne-sur-Mer, France ;  Bundesministerium für Bildung und Forschung (BMBF) ,  Germany ;  Istituto Nazionale di Fisica Nucleare (INFN), Italy ;  Stichting voor Fundamenteel Onderzoek der Materie (FOM) ,  Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO) , the  Netherlands; Council of the President of the Russian Federation  for young scientists and leading scientific schools supporting grants,  Russia ;  National Authority for Scientific Research (ANCS) ,  Romania ;  Ministerio de Economía y Competitividad (MINECO), Severo Ochoa Centre of Excellence ,  MultiDark Consolider ,  Prometeo and Grisolía programs of Generalitat Valenciana, Spain ;  Agence de l'Oriental and CNRST, Morocco.
The effects of this on our results are unknown, but we note that the absence of these taxa is not different in principle to the absence of an unknowable number of extinct taxa in the dataset. Hybridization is common in both orders: an estimated 41. 6 % species of Anseriformes and 21. 5 % species of Galliformes hybridize  [35]. Hybridization can lead to rapid shifts in phenotype that could cause uncertainty in estimating model transitions. The effect of categorizing mottled plumage as missing data is unknown. In Anseriformes, there are few species with mottled plumage whereas it is more frequent in Galliformes (Additional file 1: Figure  S1, S2 ). Given that mottled patterns do not appear to have a regular motif, categorizing these patterns is plagued by uncertainties. Having many categories for different types of mottled patterns would likely obscure a signal of a bias in the sequence of plumage pattern evolution, whereas using a single category for a pattern that exists in many states, might overly constrain the model.
Data collection and coding Current developmental evidence suggests that the default plumage phenotype in males and females in Anseriformes and Galliformes is the male plumage, in the sense that estrogen is required to produce female plumage but that testosterone is not required to make male plumage  [38]. Therefore, we collected plumage pattern information for the seven patches of plumage over the body for the males of each sample species (Fig. 2 ). We assigned the character state of each of the seven feather patches as scales, bars, spots, or an absence of patterns, following the description by Prum and Williamson  [13]. Variation in scoring plumage patterns between ten ornithologists is modest  [43]. In the study reported here all plumage patterns were scored by T-LG. Some species exhibit what appear to be longitudinal stripes along feathers, but on closer inspection are an angular version of scales with a central pigment patch, and were scored as scales, e.
Acknowledgements We would like to thank  Richard Prum  and three anonymous reviewers for insightful comments on a previous version of this manuscript,  John Endler  for supportive comments on an early version of our hypothesis, and  Paul Brakefield ,  Richard Wallbank ,  Jack Green , and  Joseph Hanly  for helpful comments. We thank  Hein van Grouw  and especially  Douglas Russell  for support during data collection at the  Natural History Museum at Tring , and  Matthew Lowe  at the  University Museum of Zoology, Cambridge  as well as  R. Kimball  and  R. Stein  for phylogeny access. Supercomputing support during preliminary testing was provided by  Stuart Rankin  at the Darwin supercomputing cluster, Cambridge and  G. Thomas ,  Sheffield , as well as  J. Teillon  and  P. Mailly ,  College de France.
, 2010). This has not been ruled out in this study, as only the peripheral blood of the cattle was tested. To conclude, this study has demonstrated that both the FMDV O 1 -Manisa and O-SKR vaccines are able to provide anamnestic response during FMDV challenge. The data from this study also showed that the current inactivated FMDV vaccine and, indeed, live-virus challenge are unable to induce a detectable memory-B-cell response in the peripheral blood. Thus, to further improve the FMDV vaccine to increase the duration of immunity, the selected antigen should seek to induce long-lived plasma and memory B-cells, signatures of a TD response. Calves, vaccination and live-virus challenge protocol. Thirteen 6-month-old conventionally reared Holstein-Friesian male calves (Bos taurus, The Pirbright Institute, Woking, UK) were split into two groups of five and a control group of three animals.
ACKNOWLEDGEMENTS The authors would like to thank the animal technicians at both The  Pirbright Institute's Compton and Pirbright Laboratories , in particular  Andy Caines  and  Bev Aldred. They  would also like to thank the FMDWRL at The  Pirbright Institute  for performing the FMDV Oserotype VNT and LPBE immunoassays. Financial support for this work was provided by the  EU Network of Animal Disease Infectiology Research (NADIR)  project (  228394 ) and the  Meat and Livestock Australia  grant  P PSH 0558.
RESULTS The structure of this GAPTrap (GT) vector is shown in Figure  1A. Genes of interest are inserted in frame with a T2A sequence  (Szymczak and Vignali, 2005; Szymczak et al. , 2004)  that replaces the GAPDH stop codon. An internal ribosome entry site  (Jang et al. , 1990 ) located immediately downstream is used to express selectable marker genes encoding neomycin, hygromycin, or puromycin resistance. In the GT vectors, sequences encoding these antibiotic resistance markers have been optimized for expression in mammalian cells and are designated Meo, Mygro, and Muro. Vectors, Addgene accession numbers and cell lines generated for this study are listed in Table  S1. To assist with the generation of targeted clones, we utilized transcription activator-like effector nucleases (TALENs)  (Wood et al.
Vectors, TALENs, and CRISPRs Vectors and cell lines described in this report can be obtained by contacting the authors (ed. stanley@mcri. edu. au or andrew. elefanty@mcri. edu. au). Some vectors will also be available through Addgene (see Table  S1 ), and may be located using the search term GAPTrap. The base GT vectors were constructed as follows. Genomic homology arms were amplified from human genomic DNA using primers and cloned into Topoblunt cloning vectors (Invitrogen). Cloned fragments representing the 5 0 arm were sequenced to ensure the GAPDH coding sequence was free from PCR-induced mutations. The 5 0 arm was cloned upstream of the 3 0 arm to create base vectors into which cassettes constituting an IRES  (Wu et al. , 1994)  and selectable marker gene were inserted. Annotated sequences of these base vectors are available in GenBank (GAPTrap-IRESMygro, GAPTrap-IRESMuro, GAPTrap-IRESMeo).
The Mygro, Meo, and Muro coding sequences were synthesized to order by GenScript (http://www. genscript. com). Individual variations derived from the base vectors, as described in Table  S1 , are available upon request from the authors. Reporter genes were introduced into specific base vectors using either conventional cloning or infusion cloning (http://www. clontech. com). In either case, the sequence of the inserted reporter was determined by Sanger sequencing (performed by the Australian Genome Research Facility) and continuity of the translational reading frame from the GAPDH coding region, through the 2A sequence to the inserted reporter, was examined using DNAStar (http://www. dnastar. com) or Snapgene (http://www. snapgene. com) DNA analysis software. TALENs against the GAPDH locus were designed and assembled by Cellectis Biosearch (http://www.
Bioluminescent Imaging and Teratoma Assays For in vivo imaging, approximately 10 6 GT-Luc2 PSCs were transplanted subcutaneously into NOD/SCID/IL2Rg-null mice  (Shultz et al. , 2005). Eight days post transplantation, mice were administered 150 mg/g of VivoGlo luciferin (Promega) by intraperitoneal injection. Five minutes after administration of luciferin, mice were anesthetized by inhalation of isoflurane (2% in oxygen) for 5 min and then imaged using a Xenogen IVIS-200 imaging system (Caliper). Images obtained were processed using Living Image software (Caliper) to normalize luciferase expression data collected across multiple time points. Teratomas for GT-lacZ iPSCs were initiated by subcutaneous injection of 10 6 cells as described by  Gropp et al. (2012). After 4-6 weeks, mice harboring a palpable mass at the injection site were killed by cervical dislocation, and the teratoma resected and fixed in 4% paraformaldehyde (PFA).
ACKNOWLEDGMENTS The authors thank  Matt Burton  and  Paul Lau  for help with flow cytometry and  Katerina Vlahos  for assistance with teratoma experiments. This work was supported by the  Victorian Government's Operational Infrastructure Support Program  and  Australian Government National Health and Medical Research Council Independent Research Institute Infrastructure Support Scheme (NHMRC IRIISS) ,  Stem Cells Australia , the  NHMRC , and the  Victoria-California Stem Cell Alliance. We are also grateful for the generous support of the  Stafford Fox Foundation. D. J. A. was supported by the  European Union 's  Seventh Framework Program  ( FP7/2007-2013)  under grant agreement  PIOF-GA-2010-276186. ). C. L. P. was supported by a  Viertel Senior Medical Research Fellowship, Australia. A. G.
1% Triton X-100, 5% normal goat serum) for at least 2 h. Primary antibodies were incubated O/N in blocking solution and then washed 3 times with PBS, 0. 1% Triton X-100 for 10 min per step to reduce unspecific binding before incubation with secondary antibodies. The Alkaline Phosphatase Detection Kit (SCR004; Millipore) was used following the manufacturers instructions. Cell growth and cell cycle analysis. To determine growth rates, 5 Â 104 cells from either WT or mutant ES cells, were plated gelatin-coated six-well plates in triplicate and counted for 4 consecutive days. For cell cycle analysis, cells were fixed in 70% ice-cold ethanol O/N, washed with PBS and then stained with propidium iodide for 4 h. Fluorescence was detected using the BD FACSCalibur flow cytometer and the obtained data were analysed with the CellQuest software.
Gene expression analyses. Total RNA from ES cells or EBs was isolated using the GenElute Mammalian Total RNA Miniprep Kit (Sigma-Aldrich). cDNA was generated using Maxima First Strand cDNA Synthesis Kit for RT-qPCR (Thermo Scientific), following the manufacture's instructions. PCR used the SYBR Green PCR Master Mix from Qiagen and the LightCycler 480 Real-Time PCR-System (Roche). Relative expression levels were normalized to GAPDH. RNA-seq analyses were performed essentially as described  47, 48. All RT-qPCR experiments were performed in triplicate. Messanger RNA-seq was performed essentially as previously described  48. Sequence reads mapped to reference genome mm9/NCBI37 using Tophat  49. Data normalization and quantification of gene expression was performed using the DESeq 2 Bioconductor package  50.
Acknowledgements We thank  L. Tora  for antibodies against  TAF1  and  TAF10 ,  S. Viville  for human ESC extracts and helpful discussions, all the staff of the IGBMC high throughput sequencing facility, a member of ' France Genomique' consortium  ( ANR10-INBS-09-08 ). This work was supported by grants from the  CNRS , the  INSERM , the  Ligue Nationale et Departementale contre le Cancer (Region Alsace) , the  Institut National du Cancer (INCa) , the  ANR-10-LABX-0030-INRT French  state fund through the  ANR  under the  programme Investissements d'Avenir labelled   ANR-10-IDEX-0002-02. ID is 'equipe labellisee' of the  Ligue Nationale contre le Cancer. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data analysis and statistical methods Data were summarized by frequency and percentage for categorical variables and by median and range for continuous variables. Markers were divided into low and high expression using the median expression score as cutoff. Correlations between markers and clinicopathological features were evaluated using the Mann-Whitney test for continuous variables and chi-square or fisher exact test for categorical variables. All survival times were calculated from the date of breast cancer surgery. Overall survival and metastasis free survival were estimated by Kaplan-Meier methods using the following first event definition: death from any cause for overall survival and distant recurrence (i. e. metastasis to any visceral or bone site) for metastasis free survival. For overall survival, patients alive at the last follow-up news were censored.
For metastasis free survival, patients who never presented with metastatic disease were censored at the last follow-up news. Univariate analysis was performed using the log-rank test to identify associations with prognostic factors. All factors considered significant at the p < 0. 05 level by this method were included in a Cox multivariate analysis to identify the major independent prognostic factors. All p values reported were two-sided. For all statistical tests, differences were considered significant at the 5% level. Multiple testing adjustment was done using Benjamini-Hochberg procedure (Supplementary Tables  S3  and S4 ). Statistical analyses were performed using STATA 11. 0 software.
Bioinformatics search 61,495 human 5'UTR sequences were downloaded from the UCSC Table Browser (hg19) (http://genome. ucsc. edu/). Sequences were read into R (v 3. 0. 2) using the Biostrings package (Pages H, Aboyoun P, Gentleman R and DebRoy S. Biostrings: String objects representing biological sequences, and matching algorithms. R package version 2. 32. 1. ) and scanned for the TAGGGW motif using the vmatchPattern function. Sequences containing that motif were then searched 5' to the TAGGGW motif for a G4 sequence. G4 were defined as a series of at least 4 matches to the pattern 'GGG' separated by gaps with width between 1 and 7 bases (custom R script adapted from the description of QFP  [29] ). Results of the 'GGG' pattern matches were first reduced (using the reduce function from the IRanges package) to merge overlapping G-stretches into single blocks.
ACKNOWLEDGMENTS We thank  Assala Lamaa  for help with RNA experiments and  Martin Dutertre  for critical reading of the manuscript. We thank  James Stevenin  for providing the antibodies against SRSF1 and  SRSF7. The work was supported by  Inserm ,  Institut Claudius Regaud and Institut Curie. This work was supported by  Ligue Nationale contre le Cancer  and  ARC (Association pour la Recherche sur le Cancer)  to SM. and AC. We also thank the  FRM (Fondation pour la Recherche Médicale)  for a post-doctoral fellowship to AC. ML was supported by the  Midi-Pyrénées Region/INSERM.
We describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy number analysis revealed frequent occurrence of massive DNA loss followed by whole genome doubling (WGD) which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68 CpG probe DNA methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers.
, 2014; Pinto et al. , 2015). Germline variants of these genes are also associated with Beckwith-Wiedemann, Familial Adenomatous Polyposis Coli and Li-Fraumeni syndromes, in which adrenocortical neoplasia occur. Comprehensive and integrated genomic characterization of ACC may identify additional oncogenic alterations, provide a framework for further research, and guide development of therapies. As one of the rare cancer projects of The Cancer Genome Atlas (TCGA), we collected the clinical and pathological features, genomic alterations, DNA methylation profiles, and RNA and proteomic signatures of 91 cases of ACC. The comprehensive nature of the dataset combined with the many easy access routes to the individual and aggregated profiles will serve as a point of reference for many future ACC and comparative cancer studies.
Clinicopathologic data are summarized in Table  S1. Patient stage at diagnosis and cortisol hypersecretion were predictive for both overall survival and disease free survival  (Else et al. , 2014b) , but age was only associated with overall survival. Gender had no clear association with disease stage or clinical outcome (Table  S1 ). We generated a comprehensive molecular dataset of the 91 tumors, as follows: whole exome sequence (n=90), mRNA sequence (n=78), miRNA sequence (n=79), DNA copy number via SNP arrays (n=89), DNA methylation via DNA methylation arrays (n=79) and targeted proteome from reverse phase protein array (RPPA; n=45) (Table  S1 ). Our analytical approach consisted of four main parts. We began by identifying somatic single nucleotide variants, gene fusions, and copy number alterations.
Given the significant copy number alterations observed, we performed an extended bioinformatic analysis that revealed the role of genome doubling and telomere maintenance in ACC. Using the various molecular datasets, we derived molecular classifications of ACC and integrated a three class solution with the somatic genomic landscape. We placed ACC in the broader context of cancer by performing several pan-cancer analyses. Finally, we quantified adrenal differentiation and tumoral hormone production across the entire cohort to correlate the genomic results with clinical parameters. All unprocessed data used in the analysis can be accessed through the TCGA portals.
Molecular classes of adrenocortical carcinoma are captured by DNA Methylation signatures To derive a robust molecular classification, we used unsupervised clustering to analyze the genomic and transcriptomic datasets (Figure  S4  and Figure  S2A ). This approach yielded four mRNA expression groups (Figure  S4A-B ), six microRNA expression groups (Figure  S4C -K), three DNA methylation groups (Figure  S4L ), three copy number groups (Figure  S2A ) and three protein expression groups (Figure  S4M-N ). Except for microRNA based clustering, the individual cluster analyses all resulted in classifications with significant differences in outcome. These subtypes are summarized in Table  S2  and characteristics of each molecular classification are described in detail in the Supplemental Information.
EXPERIMENTAL PROCEDURES Tumor and normal samples were obtained from patients after informed consent and with approval from local Institutional Review Boards (RIB). Details on all contributing Centers and their IRB approval can be found in the Supplemental Information. DNA, RNA and protein were purified and distributed throughout the TCGA network. In total, 91 primary tumors with associated clinicopathologic data were assayed on at least one molecular profiling platform. Platforms included exome sequencing, mRNA sequencing, miRNA sequencing, SNP arrays, DNA methylation arrays, and reverse phase protein arrays. Mutation calling was performed by 5 independent callers, and a voting mechanism was used to generate the final mutation set. MutSigCV (version 1. 4) was used to determine significantly mutated genes  (Lawrence et al.
, 2013). GISTIC2. 0 was used to identify recurrent deletion and amplification peaks  (Mermel et al. , 2011). Consensus clustering was used to derive miRNA, mRNA, methylation and protein subtypes. Tumor purity, ploidy and whole genome doubling were determined by ABSOLUTE  (Carter et al. , 2012). The data and analysis results can be explored through TCGA data portal (https://tcga-data. nci. nih. gov/ tcga/tcgaCancerDetails. jsp?diseaseType=ACC), the Broad Institute GDAC FireBrowse portal (http://firebrowse. org/?cohort=ACC), Memorial Sloan Kettering Cancer Center cBioPortal (http://www. cbioportal. org/public-portal/study. do?cancer_study_id=acc_tcga), Regulome Explorer (http://explorer. cancerregulome. org/all_pairs/?dataset=TCGA_ACC).
SIGNIFICANCE Adrenocortical carcinoma is a rare endocrine cancer with limited therapeutic options and overall poor outcome. We comprehensively analyzed 91 ACC specimens from four continents using state of the art genomic technologies and computational methods. In addition to identification of ACC driver genes, pathways and refined subtypes, our analysis revealed whole genome doubling (WGD) as a milestone in disease progression. Our findings suggest that ACC can serve as a model for how WGD influences disease progression. Our dataset is standardized with other TCGA studies, fully available at the TCGA Data Portal, and thus will serve as a valuable research resource. S1. S2. S3. The 25-gene signature is shown in the expression heatmap. Adrenal cortex differentiation markers are listed on the left.
SVF cells were either used directly for molecular analysis or plated and maintained in DMEM containing 10% FCS until confluence. Differentiation of primary cultures was performed according to the protocol described for hMADS cells. Isolation and analysis of RNA. These procedures followed MIQE standard recommendations and were conducted as described previously  43. Total RNA was extracted using a TRI-Reagent kit according to the manufacturer's instructions (Invitrogen). For analysis of mRNA from organs, tissues were homogenized in TRI-Reagent using an ULTRA TURRAX T25 (Ika, Germany). Two micrograms of total RNA, digested with Dnase I (Promega), were subjected to reverse transcription-polymerase chain reaction (RT-qPCR) analysis as described previously  43. The oligonucleotide sequences, designed using Primer Express software, are shown in Supplementary Table  1.
For miRNA analysis, 10 ng of total RNA was reverse transcribed using Universal cDNA synthesis kit (Exiqon). qPCR and data evaluation were performed as described previously  25, 47. U6 and 5S were used as endogenous controls. The primer sequences used are described in Supplementary Table  1. MicroRNA microarray hybridization and data analysis. The miRNA screen was performed using miRCURY LNA ™ microRNA Array Probe Set from Exiqon, to identify miRNA regulated during the "britening" of the hMADS adipocytes  48. For hybridization, 200 ng total RNA extracts from 4 biological replicates of hMADS cells differentiated at the indicated days was hybridized against a common reference pool RNA (day 9) from all samples. End-labeling of miRNAs was performed using the Exiqon Power Labeling Kit (Exiqon, Denmark) together with a synthetic spike-in controls according to the manufacturer's instructions.
Slides were hybridized over night at 56 °C in a Tecan HS 400 hybridization station, followed by automated washing and drying with nitrogen (Tecan, Austria). Immediately after drying, arrays were scanned using the GenePix 4000B microarray scanner (Axon Instruments) at 10 μ M resolution, followed by pre-processing to filter out low intensity, saturated and in homogenous spots. Background correction and normalization (global mean followed by dye-swap pair normalization) were performed, using ArrayNorm software  49. The resulting log 2 -fold changes of miRNAs (calculated against the common reference sample) were further analysed and visualized using R (www. r-project. org). Normalized as well as raw microarray data are available upon request. Histological analysis. Histological analysis was performed as previously described  44.