dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, we find that if all of the feedbacks (standard and ConvOff) are rescaled to values consistent with the observed net CRE value of -17. 1 W m -2 from the CERES EBAF (Clouds and Earth's Radiant Energy Systems Energy Balanced and Filled) dataset  [78] , then the correlation increases even further to 0. 81 (figure  1c ). This suggests that the rescaling is generally bringing the standard and ConvOff feedbacks into closer agreement, which would only be expected if the rescaling approach was working effectively. Here again the rescaled ConvOff experiments have only a slightly smaller spread than the standard experiments (a reduction again of 15% in the range and 22% in the standard deviation). It is also interesting to note that rescaling the standard experiments to have the same global mean net CRE reduces the range in their global cloud feedbacks slightly by 8% and the standard deviation by 13%, which suggests that a small part of the spread in the standard experiments might be attributable to differences in present-day cloud biases.
Acknowledgements. Thanks are due to  Tim Andrews ,  Alejandro Bodas-Salcedo ,  Robin Chadwick ,  Olivier Geoffroy ,  Gill Martin ,  Mark Ringer ,  Knut von Salzen ,  Yoko Tsushima  and  Minghua Zhang  for useful comments and discussions. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. For CMIP, the  US Department of Energy's Programme for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals.
Background: Pregnant women with asthma need to take medication during pregnancy. Objective: We sought to identify whether there is an increased risk of specific congenital anomalies after exposure to antiasthma medication in the first trimester of pregnancy. Methods: We performed a population-based case-malformed control study testing signals identified in a literature review. Odds ratios (ORs) of exposure to the main groups of asthma medication were calculated for each of the 10 signal anomalies compared with registrations with nonchromosomal, nonsignal anomalies as control registrations. In addition, exploratory analyses were done for each nonsignal anomaly. The data set included 76,249 registrations of congenital anomalies from 13 EUROmediCAT registries. Results: Cleft palate (OR, 1. 63; 95% CI, 1.
Abbreviations used ATC: Anatomical Therapeutic Chemical OR: Odds ratio TOPFA: Termination of pregnancy for fetal anomaly preeclampsia and adverse neonatal outcomes, such as low birth weight and preterm birth. 10 5] [16]  Maternal asthma exacerbations during the first trimester of pregnancy have been reported to be associated with a 50% increased risk of congenital anomalies,  17  and exacerbations during pregnancy have also been associated with other unfavorable pregnancy outcomes. 18 Treatment decisions need to balance the benefits for mother and baby of disease control against the risks related to medication use. , 12  The aim of this study is to contribute to the evidence base for clinical decision making by investigating the increased risk of specific congenital anomalies in relation to specific antiasthma medications by using data from the EUROmediCAT database for 13 EUROCAT population-based congenital anomaly registries.
Study design This study has a case-malformed control study design  19, 20  using data from EUROCAT population-based congenital anomaly registries contributing to the EUROmediCAT database. The term ''registration'' is used for all notifications in the database. Cases are congenital anomalies that have been reported as signals associated with asthma medication in the literature, and control registrations are all other congenital anomalies divided into nonchromosomal and chromosomal control registrations. An additional exploratory study was performed within the nonchromosomal control group to identify any new signals of congenital anomaly subgroups with raised risks.
Study population and data EUROCAT is a network of population-based registries collecting data on congenital anomaly registrations among live births, fetal deaths of 20 weeks' gestation or later, and terminations of pregnancy for fetal anomaly (TOPFAs). Most registries include registrations diagnosed up to 1 year after birth. Detailed descriptions of registries and the methodology have been published previously. 21, 22 The congenital anomalies are coded with International Classification of Diseases, ninth revision, or International Classification of Diseases, tenth revision, codes, and the codes are classified into standard EUROCAT congenital anomaly subgroups (EUROCAT Guide 1. 3; http://www. eurocatnetwork. eu/content/EUROCAT-Guide-1. 3. pdf). 19 Registrations with only minor congenital anomalies according to a specific list are excluded from the database (EUROCAT Guide 1.
Exposure Information on medication exposure in the first trimester of pregnancy was obtained mainly from obstetric/midwife records created before birth. Additional data sources available for some registries were the medical records of the infant, records from the general practitioner, maternity passports, and maternal interviews before or after birth. In the northern Netherlands prescription data were used as an additional data source. For Norway, the only data available for first-trimester exposure were prescription redemption records. For all registries, the first trimester of pregnancy was defined as the period from the first day of the last menstrual period to the end of gestational week 12. Exposure was defined as use of asthma medication (ATC code R03) in the first trimester of pregnancy.
Statistical analyses ORs of exposure to each of the main groups of asthma medication were calculated for each of 10 signal anomalies compared with nonchromosomal, nonsignal anomalies as control registrations. Adjusted ORs of exposure were calculated by using logistic regression with random effects for registry using the SAS 9. 3 GLIMMIX procedure. ORs were also adjusted for maternal age (categorized as <25 years, 25-29 years, 30-34 years, 35-39 years, and > _40 years), and analyses for inhaled b 2 -agonists were adjusted for use of inhaled corticosteroids and vice versa. Sensitivity analyses were adjustment for first-trimester use of systemic corticosteroids (ATC code H02AB), adjustment for period, and restriction to isolated anomalies. In the exploratory analysis within the nonchromosomal control group, the ORs of each of 62 EUROCAT congenital anomaly subgroups of exposure to asthma medications in general or to each of the 3 asthma medication classes were calculated by using all other nonchromosomal registrations in this group as control registrations.
The OR for Pierre Robin sequence of exposure to inhaled b 2 -agonists was higher than for cleft palate without Pierre Robin sequence (OR, 2. 67 [95% CI, 1. 16-6. 13] and 1. 63 [95% CI, 1. 05-2. 52], respectively). The cause for Pierre Robin sequence is thought to be heterogeneous,  29  and this finding suggests that some Pierre Robin sequences share etiologic factors with cleft palate. For the other findings, they might be explained by the large number of comparisons that have been performed, and therefore these associations could be chance findings, although this could be confirmed in independent data sets. Although we have found increased odds of cleft palate and gastroschisis after exposure to b 2 -agonists, the excess risk for the individual woman is low. The nonchromosomal prevalence of these 2 congenital anomalies in the EUROCAT registries is 8 to 9 per 10,000 births.
The main limitation is the information about medication exposure, which might be less specific according to both the dosage and timing of exposure. For one registry, medication exposure was available for births but not for TOPFAs. For another registry, the exposure data were based on information from a prescription database. Therefore we do not know whether these mothers took the medication they picked up at the pharmacy. Most importantly, although the fetuses were exposed to asthma medication, we cannot know whether it was the medication that caused the congenital anomalies or whether it was the maternal asthma because different types of antiasthma medications are used for different severities of asthma. There was limited information on potential confounding factors, but because registries collect standardized data on congenital anomalies for both exposed and unexposed cases and information on medication was obtained prospectively, the potential for bias is reduced.
Supported by the  European Union  under the  Seventh Framework Programme  (grant agreement  HEALTH-F5-2011-260598 ). The funding source had no involvement in the study. Disclosure of potential conflict of interest:  E. Garne , A. V. ,  V. Nelen ,  A. Pierini , and  H. de Walle  have received research support from the  European Union   Framework 7 Programme. M. Gatt  has received research support from the  Ministry for Health, Directorate for Health Information and Research. A. J. Neville  has received travel support from EUROCAT, is a member of the DIFK Cooper expert panel on isotrentinoin, and has received research support from  Euromedicat  (a partner is the  FP7  project) and  EURO-MEDISAFE. D. Tucker  has received travel support from  EuroMedicat. A. Wiesel  has received consultancy fees from  Geburtenregister Mainzer Modell  and has received travel support from  EuroMedicat.
We finally confirm the in vivo relevance of the newly identified IRF2BP2 co-repressor by using an IRF2BP2-deficient mouse model. Animals homozygous for the genetrap Irf2bp2 allele display an ineffective fetal liver (FL) erythropoiesis during gestation and die around birth. Thus, our data reveal a complex collaborative action of multiple co-repressor proteins within the LDB1 complex at the erythroid progenitor stage. As a result, late erythroid-specific genes are maintained in a primed state before their rapid activation upon terminal differentiation.
4b, c ). As was reported for Cbfa2t3, Irf2bp2 expression is lost upon GATA1-driven erythroid maturation in a G1E-ER model system (Fig. 4c ). In addition, using genome-wide data sets previously generated by our laboratory  5  and the Encyclopedia Of DNA Elements (ENCODE) consortium  36  , we identify two putative enhancer elements within the Irf2bp2 locus bound by the ETO2/IRF2BP2containing LDB1 complex (Fig. 4d-f ). When G1E-ER cells are differentiated by translocation of GATA1 into the nucleus, the TAL1 activator  2, 5, 32, 37  is displaced from these putative regulatory elements (Fig. 4f ), along with a loss of Irf2bp2 expression (Fig. 4c ) and RNAPII occupancy of the locus (Fig. 4f ). Collectively, these data indicate that during erythroid differentiation Irf2bp2 expression is repressed in a GATA1-dependent manner.
IRF2BP2 IRF2BP2 ARTICLE ETO2 (Fig. 5a ) or IRF2BP2 (Fig. 5b ) results in increased Alas2, Epb4. 2, Gypa and Slc22a4 expression levels, establishing the repressive roles of ETO2 and IRF2BP2 in regulating primed archetypical erythroid genes. This result also corroborates that ETO2 and IRF2BP2 form a functional erythroid co-repressor complex. In marked contrast, when performing the same experiments for LSD1 (encoded by the Kdm1a gene, Fig. 5c ), which co-occupies the same genes (Supplementary Fig. 5a ), either very minor changes (Alas2, Gypa, Slc22a4) or decreased expression (Epb4. 2) is observed. This result, together with the data derived from the reporter assays (Fig. 2d ) suggest that LSD1 does not mediate transcriptional repression by ETO2 and might even play an opposite role. To more comprehensively identify genes controlled by ETO2 and IRF2BP2, transcriptome analyses were carried out by RNA-Seq after ETO2 and IRF2BP2 depletion in MEL cells.
6 ), strengthening the hypothesis that NCOR1, through HDAC3, represses ETO2/IRF2BP2 target gene expression in erythroid progenitors. In addition, we observe a positive change in the ratio of LDB1/NCOR1 binding to the regulatory sequences of primed erythroid genes in differentiating MEL cells (Supplementary Fig. 6 ). This indicates that terminal erythroid differentiation is associated with a decreasing relative abundance of repressors such as NCOR1 as compared with activators (for example, LDB1) bound at late erythroid genes, as previously observed for ETO2 (ref. 5 ). Finally, an examination of published ChIP-Seq data  20  shows that histone acetylation levels on known NCOR1/HDAC3 target residues  44, 45  at ETO2/ IRF2BP2/NCOR1-bound erythroid genes frequently increases during erythroid differentiation (Supplementary Fig.
Antibodies used for ChIP (10 mg per experiment) are identical to those used for IP (detailed above), except for GFI1B (D-19 Santa Cruz, sc8559). Reads were mapped against NCBI build 37. 1 of the mouse genome (mm9) using Bowtie (version 2. 0. 0)  59. Uniquely mapped reads were extended to 200 bp in the 3 0 direction and were transformed into a genome-wide read density (coverage) using custom R scripts. MACS (version 1. 4. 2)  60  , CCAT (version 3. 0)  61  and in-house peak-calling software (available from the GitHub repository, https://github. com/supatt/rChIPSeqTools. git) with default parameters were used to comprehensively identify binding sites. We combined binding sites identified by all three methods to define consensus-binding regions using GenomicRanges. Binding regions predicted by each program were profiled on each genomic location.
Clustering analysis results were visualized with Java Treeview  63. After K-means clustering, we selected representative binding sites for each cobinding pattern (1,760 for ETO2-only, 2,730 for ETO2/IRF2BP2 and 5 random sets of 4,000 IRF2BP2-only). We retrieved repeat-masked 200-bp DNA sequences centred on each binding site and removed the binding sites that contain Z150 bp repeat-masked DNA. We next performed de novo motif discovery using MEME  64  with a minimum motif width of 5 bp and a maximum number of 10 motifs as the required parameters. We excluded motifs supported by r100 sites and removed repeat motifs. 'IRF2BP2-only' category motifs were selected based on their consistent identification in the five random sets. Results from MEME were subjected to an in-house ChIP-Seq analysis pipeline (available from the GitHub repository, https://github.
com/supatt/rChIPSeqTools. git) to generate motif logos and to calculate the proportion of motif containing sites. Derived motifs were then compared with known motifs in the JASPAR database  65  using Tomtom 66 with parameters: '-min-overlap 4 -dist pearson -evalue -thresh 0. 05'. Online GREAT  31  was used to assign TF-binding sites to genes and identify associated biological processes. Different GREAT analysis parameters were tested and yielded highly comparable results. Results using the 'single nearest gene method (within 1 Mb)' parameter are shown. Real-time qPCR. For gene expression analysis, RNA extractions were performed using TRIPure (Sigma) and cDNA synthesized using SuperScript II reverse transcriptase and Oligo(dT) primers (Invitrogen). ChIP DNA or cDNA were used as template in triplicate qPCR reactions (Platinum Taq DNA polymerase, Invitrogen) and analysed on a CFX96 system (Bio-Rad).
05 (Benjamini-Hochberg corrected). We selected differentially expressed genes with log2 FCs Z0. 5 and log2 FC r À 0. 5 in each KD data set to generate the correlation plots shown in Fig. 5. We selected the top 142 differentially expressed genes (log2 FC Z1 and log2 FC r À 1) from the Cbfa2t3 KD data set for the clustering analysis. We then retrieved all log2 FC values for the 142 genes in the other KD data sets. Hierarchical clustering and visualization were performed using MeV (ref. 70). For Ingenuity Pathway Analysis (Qiagen), only genes with a log2 FC Z0. 5 or r À 0. 5 and a Pr0. 05 were considered. Core Analysis (standard settings) was used to extract Gene Ontology terms that were associated with a gene set in a statistically significant fashion. Flow cytometry. E11. 5-13. 5 embryos were harvested and dissected to collect the FL.
Cells were permeabilized with 0. 1% Triton X-100, blocked with 0. 5% bovine serum albumin/ 0. 15% glycin (in PBS) and incubated overnight with ETO2 or V5 antibodies at 4 °C. After a 2-h incubation with appropriate secondary antibodies at room temperature, coverslips were mounted on glass slides with Vectashield ( þ DAPI, Vector Laboratories). Published genome-wide data sets used. The following publicly available data sets were used: LDB1, GATA1 and ETO2 ChIP-Seq data (MEL, SRA ERA000161 (ref. 5 )); RNA-Seq data (MEL/G1E/G1E-ER, ENCODE Penn State University; available at the UCSC Genome Browser (mouse genome, mm9)); p300 ChIP-Seq data (MEL, ENCODE Stanford/Yale; available at the UCSC Genome Browser (mouse genome, mm9)); H3K27Ac ChIP-Seq data (MEL/FL E14. 5/Brain, ENCODE Ludwig Institute for Cancer Research; available at the UCSC Genome Browser (mouse genome, mm9)); DNAse I-Seq data (MEL/FL E14.
5/Brain, ENCODE University of Washington; available at the UCSC Genome Browser (mouse genome, mm9)); GATA1, TAL1 and RNAPII ChIP-Seq data (MEL/G1E/ G1E-ER, ENCODE Penn State University; available at the UCSC Genome Browser (mouse genome, mm9)); microarray gene expression data (Fetal and adult erythroid populations, ErythronDB database online  35  ).
Acknowledgements We thank members of the Soler and Grosveld laboratories for helpful discussions. We thank  Anouk van Oosten  for constructing the  V5-IRF2BP2  expression plasmid and  Dr Stephen Goodbourn  ( St George's Hospital Medical School, London, UK ) for providing human IRF2BP2 cDNA,  Dr Jan van der Knaap  ( Erasmus MC, Rotterdam, the Netherlands ).
1; MatrixScience). The Mascot search algorithm (version 2. 2, MatrixScience) was used for searching against the NCBInr database (latest NCBInr release; taxonomy: Mus musculus). The peptide tolerance was typically set to 10 p. p. m. and the fragment ion tolerance to 0. 8 Da. A maximum number of two missed cleavages by trypsin were allowed and carbamidomethylated cysteine and oxidized methionine were set as fixed and variable modifications, respectively. The Mascot score cutoff value for a positive protein hit was set to 60. Individual peptide MS/MS spectra with Mowse scores below 40 were checked manually and either interpreted as valid identifications or discarded. Identified proteins were filtered against a database of background hits obtained from BirAexpressing MEL cells. Proteins with no peptides identified in the BirA control experiment or showing Mascot scores at least threefold higher than the control were considered as positive hits, and were further validated by conventional co-IP experiments (Fig.
Additional information Accession codes: Nucleotide sequences for ChIP-Seq and RNA-Seq data sets have been deposited in the Gene Expression Omnibus (GEO) database under accession code GSE59859. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium/PRIDE partner repository under accession code PXD001892. Supplementary Information accompanies this paper at http://www. nature. com/ naturecommunications Competing financial interests: The authors declare no competing financial interests. This work is licensed under a Creative Commons Attribution 4. 0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
We add an interpretable semantics to the paraphrase database (PPDB). To date, the relationship between phrase pairs in the database has been weakly defined as approximately equivalent. We show that these pairs represent a variety of relations, including directed entailment (little girl/girl) and exclusion (nobody/someone). We automatically assign semantic entailment relations to entries in PPDB using features derived from past work on discovering inference rules from text and semantic taxonomy induction. We demonstrate that our model assigns these relations with high accuracy. In a downstream RTE task, our labels rival relations from WordNet and improve the coverage of a proof-based RTE system by 17%.
In fact, in the most recent RTE challenge, over half of the submitted systems used WordNet  (Pontiki et al. , 2014). Even the NatLog system (MacCartney and  Manning, 2007) , which popularized natural logic for RTE, relied on WordNet and did not solve the problem of assigning natural logic relations at scale. The main contributions of this paper are: • We add a concrete, interpretable semantics to the Paraphrase Database (PPDB)  (Ganitkevitch et al. , 2013) , the largest paraphrase resource currently available. We give each entry in the database a label describing the entailment relationship between the phrases. • We develop a statistical model to predict these relations. The enormous size of PPDBover 77 million phrase pairs!-makes it impossible to perform this task manually. Our wide range of monolingual and bilingual features results in high intrinsic accuracy.
(2006)  used dependency parses to automatically learn such patterns, which they used to augment WordNet with new hypernym relations. Similar monolingual signals have been used to learn fine-grained relationships between verbs, such as enablement and happensbefore  (Chklovski and Pantel, 2004; Hashimoto et al. , 2009). Recognizing Textual Entailment The shared RTE tasks  (Dagan et al. , 2006)  have been a springboard for research in natural language inference, Figure  2 : Distribution of entailment relations in different sizes of PPDB. Distributions are estimated from our manual annotations of randomly sampled pairs. PPDB-XXXL contains over 77MM paraphrase pairs (where the majority type is independent), compared to only 700K in PPDB-S (where the majority type is equivalent). using data motivated by the applications to information retrieval, information extraction, summarization, machine translation evaluation, and more recently, question answering  (Giampiccolo et al.
The Paraphrase Database (PPDB) PPDB is currently the largest available collection of paraphrases. Compared to other paraphrase resources such as the DIRT database (12 million rules)  (Lin and Pantel, 2001)  and the MSR paraphrase phrase table (13 million)  (Dolan et al. , 2004) , PPDB contains over 150 million paraphrase rules covering three paraphrase types-lexical (single word), phrasal (multiword), and syntactic restructuring rules. We focus on lexical and phrasal paraphrases, of which there are over 77 million rules. Of these, a large fraction are true paraphrases-either equivalent (distant/remote) or asymmetric entailment (girl/little girl)-but many are not. PPDB contains some pairs which are related by semantic exclusion (nobody/someone), some of which are related by something other than entailment (swim/water), and some which are simply unrelated (car/family).
Selection of Paraphrases In this paper we focus on paraphrases pairs from PPDB that occur in RTE data. We use the recent SICK dataset from in the 2014 SemEval RTE challenge  (Marelli et al. , 2014)  for our experiments. The data consists of 10K sentences split roughly evenly into training and testing sets. The sentence pairs are labeled using a 3-way entailment classification: ENTAILMENT, (29%) CONTRADIC-TION (15%), or NEUTRAL (56%). We consider all phrase pairs from PPDB hp 1 , p 2 i up to three words in length such that there is some T/H sentence pair in which p 1 appears in T and p 2 appears Lexical We use the lemmas, POS tags, and phrase lengths of p1 and p2, the substrings shared by p1 and p2, and the Levenstein, Jaccard, and Hamming distances between p1 and p2. Distributional Given a dependency context vectors for p1 and p2, we compute the number of shared contexts, and the Jaccard, Cosine, Lin1998, Weeds2004, Clarke2009, and Szpektor2008 similarities between the vectors.