dataesr/scientific-paragraphs-categorization huggingface dataset:
After transfer to nitrocellulose membrane with a semi-dry system, proteins were detected by hybridization with appropriate antibodies. For polysome analysis, Western blot signals were quantified using Image Lab software (version 5. 2. 1 build 11-Bio-Rad). We used the square volume tool to define boxes of identical size at the expected position of the protein band we wanted to quantify. We also defined a series of blank boxes for each lane for background definition. To normalize the results, we calculated the percentage of signal over all the fractions of a run for both Nmd4 and Rps8 (control). Statistical significance of the signal differences between conditions was assessed by the paired Student t-test.
For example, Dcp1 purification also showed the enrichment of another decapping-associated factor, Pby1 (Fig 2F). The results, including the variability of biological replicates, are presented in Dataset EV1 and can be explored at hub05. hosting. pasteur. fr/NMD_complexes. ◀ Figure 2. Purification of NMD factors reveals two distinct complexes containing Upf1. A-F Enrichment values for proteins identified in tagged Upf1 (A), Upf2 (B), Upf3 (C), Nmd4 (D), Ebs1 (E) and Dcp1 (F) purifications. Tagged proteins are indicated in bold for each experiment. Black bars correspond to average enrichment values obtained in purifications done without RNase and orange bars with an RNase A and RNase T1 treatment. Error bars represent SD. Dots represent individual enrichment values for each protein in the six replicates for Upf1-TAP, 3 for Upf1-TAP and RNase, 6 for Upf2-TAP, 3 for Upf2-TAP and RNase, 5 for Upf3-TAP, 3 for Upf3-TAP and RNase, 4 for Nmd4-TAP, 3 for Nmd4-TAP and RNase, 3 for Ebs1-TAP, with and without RNase and 3 for Dcp1-TAP with or without RNase.
Acknowledgements We thank  Magalie Duchateau ,  Julia Chamot-Rooke  and  Mariette Matondo  of the  Mass Spectrometry for Biology UtechS at the Pasteur Institute  for access to the mass spectrometry facility,  Jean-Yves Coppée , from the  Transcriptome and Epigenome, Biomics  platform for access to the sequencing facility. We thank  Giorgio Dieci ,  University of Parma, Italy , for the Rps8 antibodies. We thank members of the  Genetics of Macromolecular Interactions Unit :  Gwenael Badis  for providing RNA sequencing results for the upf1D strain, helpful discussions and critical reading of the manuscript;  Antonia Doyen  for performing initial affinity purification experiments and for building strains;  Frank Feuerbach  for providing strains, reagents, critical suggestions, advice and support in writing the manuscript; and  Micheline Fromont-Racine  for discussions and critical reading of the manuscript.
We thank  Christophe Malabat  from the  Bioinformatics and Biostatistics hub, Pasteur Institute , for help with data analysis and support in making the results available through a web interface. The study was supported by the  ANR   CLEANMD grant  ( ANR-14-CE10-0014 ) from the  French Agence Nationale de la Recherche  to C. S. , A. J. and H. L-H. and by the  Ministère de l'Enseignement Supérieur et de la Recherche  and the "Fondation ARC pour la recherche sur le cancer" PhD fellowships to M. D. and J. K. , and by continuous financial support from the  Institut Pasteur  and  Centre National de Recherche Scientifique, France.
Data availability • The accession number for the sequencing data reported in this paper is GEO: GSE102099 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE102099). • The MS proteomics data that support the findings of this study have been deposited in the ProteomeXchange Consortium via the PRIDE repository (Vizcaı ´no et al, 2016) with the dataset identifier PXD007159 (http://proteomecentral. proteomexchange. org/cgi/ GetDataset?ID=PXD007159). In addition to the access to raw data, enrichment and intensity data are available as an interactive web application at: https://hub 05. hosting. pasteur. fr/NMD_complexes. Expanded View for this article is available online.
Antisense transcription is known to have a range of impacts on sense gene expression, including (but not limited to) impeding transcription initiation, disrupting post-transcriptional processes, and enhancing, slowing, or even preventing transcription of the sense gene. Strand-specific RNA-Seq protocols preserve the strand information of the original RNA in the data, and so can be used to identify where antisense transcription may be implicated in regulating gene expression. However, our analysis of 199 strand-specific RNA-Seq experiments reveals that spurious antisense reads are often present in these datasets at levels greater than 1% of sense gene expression levels. Furthermore, these levels can vary substantially even between replicates in the same experiment, potentially disrupting any downstream analysis, if the incorrectly assigned antisense counts dominate the set of genes with high antisense transcription levels.
Our results show that spurious antisense reads are often present in experiments, and can manifest at levels greater than 1% of sense transcript levels. Furthermore, the number of spurious antisense reads can vary substantially between replicates within the same experiment. In some cases, this variation may be sufficient to disrupt downstream analysis of antisense gene expression, by causing spurious antisense counts to dominate the set of genes with high antisense transcription levels. To detect and correct for wrongly assigned reads we developed a tool, RoSA (Removal of Spurious Antisense), which calculates an antisense correction factor by identifying subsets of reads where all antisense reads are spurious. We evaluate the effect of using RoSA on Arabidopsis thaliana experimental data where varying levels of spurious antisense were present in different replicates.
Methods As noted by  Jiang et al. (2011, 25 ) , spurious antisense read counts can be estimated by analysing either ERCC spike-in data or counts of sense and antisense reads around splice sites. Each approach has different advantages: using spike-ins is simpler and faster, while using spliced reads allows a gene-by-gene estimate to be made. RoSA implements both approaches, in conjunction with pre-processing scripts to generate specialised read counts required by the tool. Once RoSA has an estimate of the levels of spurious antisense, it can adjust the raw antisense counts to account for the incorrectly stranded reads.
Counting antisense reads. In order to apply the antisense correction factor, counts of antisense reads for each gene are required. Counting antisense reads is not directly supported by read counting tools. However, it can be performed with feature-Counts  37  by setting parameters to indicate that reads are stranded in the opposite direction to which they are. Unfortunately, if there are overlapping genes then reads in the overlaps will be counted twice using this tactic. As reads in regions of gene overlap are necessarily ambiguous, they cannot be considered to be antisense, spurious or otherwise. RoSA avoids this issue by building a custom antisense annotation based on the input sense annotation but excluding regions where genes on opposite strands overlap. Different gene transcripts are accounted for by merging all transcripts for a gene into a single maximal transcript.
Conclusions Spurious antisense is common in strand-specific RNA-Seq datasets and can occur at varying levels across replicates in the same experiment. Differing levels of such incorrectly assigned reads are enough to disrupt differential expression analyses of antisense gene expression. We have developed a new tool, RoSA, which can detect, quantify and correct for spurious antisense. RoSA provides an important quality control step for researchers analyzing antisense expression in their data.
/doi. org/10. 12688/f1000research. 18952. 1 07 Jun 2019, :819 ( Latest published: 8 ) https://doi. org/10. 12688/f1000research. 18952. This article is included in the gateway. RPackage This article is included in the Python Collection collection. Gordon Simpson ( ), Geoffrey J. Barton ( ) Corresponding authors: G. G. Simpson@dundee. ac. uk g. j. barton@dundee. ac. uk : Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Author roles: Mourão K Writing -Original Draft Preparation, Writing -Review & Editing; : Conceptualization, Investigation, Methodology, Project Schurch NJ Administration, Software, Supervision, Writing -Review & Editing; : Data Curation, Investigation, Writing -Review & Editing; Lucoszek R : Conceptualization, Methodology, Software, Writing -Review & Editing; : Data Curation, Investigation, Writing -Froussios K MacKinnon K Review & Editing; : Data Curation, Writing -Review & Editing; : Funding Acquisition, Writing -Review & Editing; : Duc C Simpson G Barton GJ Funding Acquisition, Project Administration, Resources, Supervision, Writing -Review & Editing No competing interests were disclosed.
1 v1 1 /doi. org/10. 12688/f1000research. 18952. 1 07 Jun 2019, :819 ( Latest published: 8 ) https://doi. org/10. 12688/f1000research. 18952. This article is included in the gateway. RPackage This article is included in the Python Collection collection. Gordon Simpson ( ), Geoffrey J. Barton ( ) Corresponding authors: G. G. Simpson@dundee. ac. uk g. j. barton@dundee. ac. uk : Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Author roles: Mourão K Writing -Original Draft Preparation, Writing -Review & Editing; : Conceptualization, Investigation, Methodology, Project Schurch NJ Administration, Software, Supervision, Writing -Review & Editing; : Data Curation, Investigation, Writing -Review & Editing; Lucoszek R : Conceptualization, Methodology, Software, Writing -Review & Editing; : Data Curation, Investigation, Writing -Froussios K MacKinnon K Review & Editing; : Data Curation, Writing -Review & Editing; : Funding Acquisition, Writing -Review & Editing; : Duc C Simpson G Barton GJ Funding Acquisition, Project Administration, Resources, Supervision, Writing -Review & Editing No competing interests were disclosed.
Background: Healthcare policy and academic literature have promoted improving the transitional care of young people leaving child and adolescent mental health services (CAMHS). Despite the availability of guidance on good practice, there seems to be no readily accessible, coherent ethical analysis of transition. The ethical principles of non-maleficence, beneficence, justice and respect for autonomy can be used to justify the need for further enquiry into the ethical pros and cons of this drive to improve transitional care. The objective of this systematic review was therefore to systematically search for existing ethical literature on child-to adult-orientated health service transitions and to critically appraise and collate the literature, whether empirical or normative. Methods: A wide range of bioethics, biomedical and legal databases, grey literature and bioethics journals were searched.
Search strategy The overall search strategy was agreed by MP/LO/PT/CS/ FM but LO/PT/MP undertook searches of specific databases and journals. The following bioethics and biomedical databases were searched: Project MUSE, PubMed, PsycINFO, CINAHL, EMBASE and Web of Science (previously known as Web of Knowledge). Two legal databases, Lexis Library and Westlaw UK, were also searched as some jurisprudential arguments indicate that ethics is an integral part of the law  [23]. Ancillary searches included a systematic search of the Grey literature, including ProQuest Dissertations and Theses and Google Scholar. We also undertook ancestral searches (references of any reviews and papers identified through searching databases and the Grey literature); forward searches for articles citing key papers or other documents; and searches of specific specialist journals published since January 2000.
Data extraction and mapping MP/LO/PT checked titles and abstracts to assess whether articles fulfilled inclusion/exclusion criteria and removed duplicates. Any doubts about meeting inclusion criteria were resolved through retrieval of full articles and team discussion (MP/LO/PT/CS/FM). Full versions of the yield were read by a researcher (MP, LO or PT), who checked against inclusion and exclusion criteria, to identify systematic reviews, reviews, ethical analyses, legal analyses and primary research. Uncertainty about inclusion was resolved through review of each paper by two others from the reviewing group (MP/LO/PT). Ancestral searches of the references of reviews and identified documents were undertaken (MP/LO/PT) and repeated recurrently. Experts in the field were also consulted about additional reviews and primary research.
Could the methodology have been better? The systematic search strategies used for finding empirical research were thorough and robust. One improvement when searching for argument-based ethics literature could have been to frame our fifth research question in a PICO (population, intervention, comparator and outcome) format  [38]. If we were to repeat the systematic review, we would not search the legal databases, given the large numbers of titles identified that ultimately did not provide a yield. Alternatively, perhaps a systematic review is not the best way to identify or collate relevant ethical issues. McDougall (2015)  [39]  has proposed that a good bioethics literature review does not have to be systematicinstead it should capture and analyze the key ethical ideas of relevance to the research question, i.
Acknowledgements Samantha Johnson, Librarian ,  University of Warwick ;  Ingrid Holme , for proof reading the article; Current core members of the  MILESTONE Consortium :  Swaran Singh ,  Helena Tuomainen ,  Jason Madan ,  Jane Warwick ,  Cathy Street ,  Dieter Wolke ,  Moli Paul ,  Priya Tah ,  Rebecca Appleton ,  Alastair Canaway ,  James Griffin  ( University of Warwick, UK ),  Giovanni de Girolamo ,  Giulia Signorini  ( Saint John  of  God Clinical Research Center, Italy) ,  Paramala Santosh ,  Ilyas Sagar-Ouriaghli ,  Natalie ,  Federico Fiori  ( Kings College London, UK ),  Diane Purper-Ouakil ,  Frédérick Russet ,  Virginie Maurice ,  Véronique Humbertclaude  ( Hôpital Saint Eloi, France ),  Athanasios Maras ,  Larissa van Bodegom ,  Mathilde Overbeek ,  Esther Kooymans  ( Yulius Academie, the Netherlands ),  Ulrike Schulze ,  Melanie Saam ,  Ulrike Breuninger  ( Universität, Germany ),  Sabine Tremmery ,  Gaëlle Hendrickx  ( KU Leuven, Belgium ),  Fiona McNicholas ,  Aleksandra Gronostaj  ( University College Dublin, Ireland ),  Tomislav Franić ,  Nikolina Davidović  ( University Hospital Split, Croatia ),  Frank Verhulst ,  Gwen Dieleman ,  Suzanne Gerritsen  ( Erasmus MC, The Netherlands ),  Kate Lievesley (HealthTracker, UK) ,  Amanda Tuffrey ,  Anna Wilson ,  Charlotte Gatherer ,  Leanne Walker  ( Young  project advisors).
The journals searched were Ethics (previously known as International Journal of Ethics and Journal of Ethics, <April 2016); Ethics and Behaviour (<May 2016); Ethical Theory and Moral Practice, Journal of Medical Ethics (and supplements), Philosophy Ethics and Humanities in Medicine (from first volume in March) and the Hastings Centre Report (all<June 2016); and Philosophy Psychiatry and Psychology (<September 2015). Search terms Key words and their truncations and relevant database- specific subject headings and [MeSH terms] were used, tar- geting all four of the following subject areas on an AND/ OR basis: Transition: including transition*; interface*; transfer; Care continuity; Continuum of care; Care continuum; Patient care continuity [Transition to adult care; Continuity of patient care; Referral and consultation]; becom* adult; "CAMHS to AMHS"; "child and adolescent mental health services to adult mental health services" Age: including young; youth*; teen*; adolesce*; young adult; youth; transition age youth; emerging adult*, or age-group criteria (e.
Data are presented as means ± standard deviation or absolute values. Statistica 10 software (StatSoft Inc. , USA) was used for the statistical analysis. The Mann-Whitney U test was performed to compare age, weight, duration of surgery, blood loss, and pain intensity between independent samples. For the multiple sample analysis, a Kruskal-Wallis test was used. The pain intensity between dependent samples was compared through Friedman analysis of variance (ANOVA; multiple samples) and the Wilcoxon test (two samples). A chi-square test was used to compare ASA physical status, gender, and type of surgery. We assessed pain syndrome frequency using chi-square and Fisher tests for independent samples and Cochran's Q test for dependent samples. To calculate the required sample size, we considered the study results of Richardson et al.
In addition to DNA from biotinylated chromatin, the sequencing was repeated using an input DNA (total digested chromatin) as a control. The sequencing data reported in this paper have been deposited in the Sequence Read Archive at the National Center for Biotechnology Information, (www. ncbi. nlm. nih. gov/sra, accession no. SRR6234698, SRR6234967 and SRR6235028). For data analysis reads were aligned to the human genome (hg19/GRCh 37). HeLa LADs were obtained from GEO datasets (GSE57149). Downstream analysis and visualization was performed using Seqmonk analysis tool (https://www. bioinformatics. babraham. ac. uk/ projects/seqmonk/). For visualization in genome browser images, the data were calculated as the log2 IP/input ratio across 10kb bins with smoothing correction applied for 5 adjacent probes.
The scale for all genome browser images is log 2 ± 2 with the exception of Figure  3D , which is log 2 0-2. For Monte Carlo simulations, the genome was divided into larger 100kb fragments and regions overlapping LADs were tested against the genome in 10 000 simulations. HeLa RNA-seq data from the capped analysis of gene expression technique, as well as ChIP-seq data for Pol II, H3K4me3, H3K27ac and H3K36me3 were obtained from the ENCODE download portal (http://genome. ucsc. edu/encode/downloads. html).
To assess the robustness of our approach, we compared biotinylation patterns at various genomic loci between different BAP-histone fusions using quantitative PCRs (qPCRs). In this regard, we performed PUB-NChIP followed by qPCRs in HeLa cells stably co-expressing BirA-emerin and BAP-H2A or BAP-H2AZ or BAP-macroH2A1. We designed qPCR primers for regions showing high enrichment in PUB-NChIP-seq data from different chromosomes (chr2, 3, 4, 5, 7, 10, 13, 15, 19) (Supplementary Figure  S6 ). Our results indicated that cells expressing BAP-macroH2A1 consistently showed high enrichment at all the tested loci while BAP-H2A and BAP-H2AZ expressing cell lines showed slightly lower enrichment. These results support the notion that Topokaryotyping can be performed using other BAP-fused histones (canonical or variant).
These results indicate that NE-association of der19 with Emerin was decreased upon stress exposure, however, importantly, the overall relationship between associations of different variable EADs was not affected. Finally, to confirm the changes observed in EADs shown in Figure  5B , we performed PUB-NChIP followed by qPCRs in HeLa cells co-expressing BirA-emerin and BAP-macroH2A1. We designed qPCR primers for genomic regions showing high enrichment in PUB-NChIP-seq dataset mainly from chromosome 3 and 19 (der19) but also from chromosomes 2 and 7. We compared the enrichment of these regions with and without IR treatment in a similar experimental setup as described in Figure  5B. The results are consistent with our IF-based analysis where we saw no change in three loci from chromosome 3, which most likely belonged to constant EADs while there is a consistent reduction in 3 different loci from chromosome 19p (variable EADs), which as in Figure  5B  showed a reduction in Emerin binding after cellular treatment with IR.
ACKNOWLEDGEMENTS We are grateful to  Dr Vassilis Doucas  for his constructive discussions in the early stages of the project and  Ms Sophie Salome-Desnoulez  for assistance in confocal microscopy. We thank  Dr Alain Bernheim  for his valuable advices in cytogenetics and  Dr Joelle Wiels  for reading the manuscript. We thank  Ms Daria Marinovic  for assistance in artwork for the figures. We would also like to thank  Dr Undine Mechold  for useful discussions. We state and sincerely lament that  Dr Vasily Ogryzko  passed away during the course of the study. Authors contributions: V. O. and M. S. conceived and designed the study. AJurisic, C. R. and M. S. designed and performed the experiments and interpreted the data. B. S. and AJauch performed and interpreted M-FISH experiments. P. T. performed PUB-NChIP-seq experiments.
DATA AVAILABILITY PUB-NChIP-seq raw and processed data have been deposited in the NCBI gene expression omnibus (GEO) under accession number GSE118996. Also, the sequencing data reported in this paper have been deposited in the Sequence Read Archive at the National Center for Biotechnology Information, (www. ncbi. nlm. nih. gov/sra, accession no. SRR6234698, SRR6234967 and SRR6235028). For data analysis reads were aligned to the human genome (hg19/GRCh 37). HeLa LADs were obtained from GEO datasets (GSE57149). HeLa RNA-seq data from the capped analysis of gene expression technique, as well as ChIPseq data for Pol II, H3K4me3, H3K27ac and H3K36me3 were obtained from the ENCODE download portal (http: //genome. ucsc. edu/encode/downloads. html).
Senescent cells display decreased expression of DNA repair genes To gain some insight into the mechanisms regulating cellular senescence in epithelial cells, which are at the origin of most cancers, we characterized the transcriptome of immortalized human mammary epithelial cells expressing a fused inducible (by 4-OHT) MEK:ER oncogene (HMEC-MEK), a model of oncogene-induced senescence (OIS) that we have described previously  [17] [18] [19]. Gene ontology (GO) analyses revealed that expression of numerous genes involved in pathways covering different DNA repair systems were strongly downregulated during OIS in HMEC-MEK cells (Table  1 ). To verify whether this downregulation of DNA repair gene is specific to either this cell type or to the senescence inducers we examined other publicly available transcriptomic datasets: replicative senescence in human umbilical vein endothelial cells (HUVEC) (HUVEC-RS)  20  , genotoxic stress (etoposide)induced senescence in immortalized human fibroblasts (WI38-ETO)  21  , and RAS-induced senescence in human fibroblasts (IMR90-RAS)  22.
1c ). Finally, we confirmed that this decrease at the mRNA level resulted in a decrease at the protein level (Fig. 1d, e ). Altogether, these data suggest that a decreased expression of DNA repair genes is a hallmark of cellular senescence. Repression of DNA repair genes in senescent cells is mediated by the RB pathway RB and E2F transcription factors are both master regulators of cellular senescence and the expression of DNA repair genes  23. Therefore, we examined whether this Immortalized human mammary epithelial cells expressing a fused inducible MEK:ER oncogene (HMEC-MEK) was induced to senesce by adding 4-OHT. Microarray analyses (Agilent technology) were performed in control and senescent cells and Gene ontology (GO) analysis has been performed using Genespring software (Agilent technology) on the downregulated genes.