dataesr/scientific-paragraphs-categorization huggingface dataset:
Strain distribution analysis of culturing surface by FEM The numerical analysis of the strain distribution of the culturing surface was carried out by FEM. The FE model of the chamber is shown in Figure  3(A). A direction of the x axis corresponds to the stretching direction and the y axis corresponds to the perpendicular to the stretching direction in the coordinate system. Numerical calculation was carried out by FEM code LS-DYNA ver. 971 by LSTC (Livermore Software Technology Corporation, CA, USA) and the Jvision 3. 0 by JSOL (Tokyo, Japan) was used as the preand post-processor. The material property of the chamber model is shown as follows  [18]. The density: 9. 2 × 10 2 kg/m 3 , Young's modulus: 0. 75 MPa, Poisson's ratio: 0. 49 The results of numerical analysis for the stretching are shown in Figure  3 (B) and (C).
Image acquisition was performed by using an LAS-1000 charge-coupled device (CCD) camera and Image Gauge software version 4. 22 (Fuji Photo Film Co). Binding and internalization assays. HeLa cells were washed with PBS and detached by incubation with PBS-EDTA. After 30 min of saturation in binding buffer (RPMI, 3% BSA), cells were incubated for 1 h at 4°C with the 17D or Asibi strain in binding buffer. Cells were washed twice with cold binding buffer and once with PBS to remove unbound particles. Total RNA was extracted from cells. For internalization experiments, cells were chilled on ice prior to incubation with YFV for 1 h at 4°C. Cells were extensively washed with PBS to remove unbound viral particles. Virus internalization was initiated by shifting the cells to 37°C for 2 h. Alternatively, cells were kept for 2 h at 4°C to prevent virus internalization.
5 g) using random primers (Roche) and SuperScript II reverse transcriptase (Invitrogen), according to the manufacturers' protocols. Samples were treated with RNase H (Biolabs). Genomic viral RNA was quantified by real-time qPCR on an ABI Prism 7300 sequence detection system using the SYBR green PCR kit according to the manufacturer's instructions (Applied Biosystems). The primers used for qPCR were designed with PrimerExpress 2. 0 software (Applied Biosystems) and are listed in Table  S1  in the supplemental material. Relative expression quantification was performed based on the comparative threshold cycle (C T ) method, using GAPDH as the endogenous reference control. Genome equivalent (GE) concentrations were determined by extrapolation from a standard curve obtained with serial dilutions of the pACNR-113.
Briefly, cells were fixed with 4% PFA and permeabilized with 1% saponin. Slides were mounted with ProLong Gold antifade reagent containing 4=,6-diamidino-2-phenylindole (DAPI) for nuclei staining. Samples were analyzed using an upright Zeiss Observer microscope system (Carl Zeiss, Jena, Germany) equipped with an z-series of 1-m optical sections were acquired, and single optical sections through the cells are depicted. Image analysis was done with AxioVision imaging software version 4. 4 (Carl Zeiss, Germany). Analyses were performed at least twice, and at least 100 cells were visualized. For endocytosis quantification, Hep2␤ cells were incubated with transferrin coupled to Alexa Fluor 488 (TfA488, Molecular Probes) and anti-IL-2R␤ coupled to Cy3 for a time course up to 15 min at 37°C to allow receptor entry  (70).
ACKNOWLEDGMENTS We thank  L. Pelkmans  for the HelaMZ cells,  C. Rice  for the Huh7. 5 cells and the pACNR-113. 16plasmid,  E. Meurs  for Huh7 cells,  C. Neuveut  for the HepG2 cells and HFFs,  P. Bredenbeek  for the pACNR-FLYF17DII plasmid,  A. Dautry-Varsat  for Eps15 plasmids,  A. Helenius  for Cav1 and dynamin-2 vectors, and  T. C. Pierson  for the subgenomic WNV replicon and plasmids containing WNV structural genes. We are also thankful to  Sandy Simon  and  Olivier Schwartz  for critical reading of the manuscript. This project was supported by grants from the  Fondation pour la Recherche Médicale , the  French National Research Agency (ANR)  ( ANR-10-IHUB-0002 ,  ANR-12-JSV3-003-01 , and " Investissements d'Avenir" program   ANR-10-IHUB-0002 ),  Ville de Paris EMERGENCES Program, Marie Curie International Reintegration program   PCIG11-GA-2012-322060 , the  INTRAPATH program  of the  Marie Curie FP6 framework , the  Pasteur Institute , the  Institut Universitaire d'Hématologie , and the  Institut National de la Sante et Recherche Médicale.
FUNDING INFORMATION Ville de Paris Emergences program provided funding to Nolwenn Jouvenet. Marie Curie International Reintegration program provided funding to Nolwenn Jouvenet under grant number PCIG11-GA-2012-322060. Intrapath program of the Marie Curie FP6 framework provided funding to Ali Amara. Institut universitaire d'Hematologie provided funding to Ali Amara. La Caixa Foundation provided funding to Maria Dolores Fernandez-Garcia. French Ministry of Research provided funding to Ophélie Dejarnac. Institut Pasteur provided funding to Nolwenn Jouvenet. Agence Nationale de la Recherche (ANR) provided funding to Ali Amara under grant numbers ANR-10-IHUB-0002 and ANR-10-IHUB-0002. Agence Nationale de la Recherche (ANR) provided funding to Nolwenn Jouvenet under grant number ANR-12-JSV3-003-01.
Whole-Genome Sequencing and RNA-Seq from 23 Breast Cancer Samples To understand the inter-relationships between somatic mutation and the transcriptome, we matched RNA-seq data to whole-genome-sequencing data in 23 breast cancer samples. Of these, 14 were primary breast cancers and nine were matched breast cancer cell lines. For the genomes, tumor samples were sequenced to $403 coverage and matched normal samples to $303 coverage, with somatically acquired substitutions, insertions or deletions (indels), genomic rearrangements, and copy-number changes called by a suite of in-house algorithms. The whole-genome sequencing for the 14 primary breast cancer samples has been described previously  (Nik-Zainal et al. , 2012a , 2012b) , although improvements in our bioinformatics algorithm allowed us to update the list of genomic rearrangements (Table  S1 ).
We implemented a seed-and-extend mapping algorithm to find reads that span different regions of the genome, and then we developed a discordant pair analysis algorithm, drawing these results together with a set of methods to arrange the results into biologically meaningful categories (described in detail in the Experimental Procedures and Figure  S1 ). The primary advantage of our software pipeline, which we call RNA Architect, is the comprehensive detection of transcriptional alterations, including events missed by other methods. These would include compound events present in cis, such as fusion transcripts involving alternative splice forms and exon skips with cryptic splice sites; internal exon shuffling (reusage); posttranscriptional modifications, such as early polyadenylation sites; and non-canonical transcript junctions, for example, fusions between the sense and antisense of different genes or those involving lowly expressed transcripts that are not present in reference databases.
For each rearrangement, we identified any aberrant transcript arising from the genes involved, excluding any splice form seen in the normal breast organoids or the Ensembl database. Even in cancer samples without rearrangements affecting a given gene, we often found evidence for previously undocumented transcripts, such as novel splice forms, read-through transcripts, and non-canonical splice acceptor or donor sites. It is, therefore, difficult to argue categorically for a given rearrangement that an abnormal transcript arises as a direct consequence of the genomic change. Instead, since we are more interested in the overall patterns of abnormal transcription caused by somatic mutation, we studied the excess of aberrant transcription associated with the different categories of genomic rearrangement.
In 133 of 501 (27%) such events, the resulting exon structure would be predicted to generate an in-frame gene from transcript isoforms reported in Ensembl; we found RNA-seq reads supporting these in-frame transcripts in 35 of 133 (26%). Of the 368 rearrangements pre- dicted to be out of frame or involve the non-coding UTR, we found evidence for in-frame transcripts in 25 (7%). In many cases, the in-frame transcript was more heavily expressed than the canonical, out-of-frame transcript, suggesting that nonsense-mediated decay may be acting on the latter (Figure  4C ). Overall then, these data indicate that 60 of 501 (12%) of genomic rearrangements reordering exons of one or two genes in the same orientation have the potential to generate transcripts encoding in-frame proteins. Many of these are expressed at appreciable levels, mostly driven by the upstream regulatory apparatus.
EXPERIMENTAL PROCEDURES Detection of Changes to Gene Transcript Structure We developed a suite of tools for the analysis of cancer RNA-seq data, called RNA Architect, comprised of several algorithms. RNA Architect's central component is a seed-and-extend algorithm used to find reads that span disparate regions of the genome. These are junction reads that provide the breakpoint between non-adjacent loci of fusion genes or alternative splice forms. We describe below how we obtained these highly informative split reads by pre-filtering; grouping the reads into common events; and then classifying, annotating, and filtering the high-confidence events (Figures  S1A  and S1B ). (1) Pre-filter. We first removed any read that mapped normally to a genic region in the human genome (build 37). That is, the read was fully contained in an exon or was split across adjacent exons.
(  6 ) Annotate. Having resolved the breakpoints of all fusion genes, aberrant splice forms, and compound events, Architect writes junction coordinates into a database (SQLite) and the split reads into a binary alignment file (BAM). Each event, which represents the junction of two or more expressed sequences that are not normally found to be adjacent, was ranked across a number of measures, including whether the event is seen in normal samples or in the reference genome; the number of reads supporting the event (total and unique); the sequence context of the junction (i. e. , whether canonical donor-acceptor sequences are used); the average number of unique bases per read; and the number of reads mapped in the direct and reverse complemented orientation. We then implemented separate processes that used these measures to arrange the events into the following biologically meaningful categories: (A) Exon skips.
A junction between two non-adjacent exons from the same transcript, not found in normal organoids (less than two reads) or in Ensembl, where the first exon is 5 prime to the second exon. Exon skips were annotated to the Ensembl transcript that involved the fewest number of exons lost. We required a split forward read and a split reverse read to call an exon skip. We subcategorized exon skips into those that involved the canonical edges of the exons and those that involved a cryptic splice site (either 5 0 or 3 0 of the donor or acceptor site). (B) Exon reusages. A junction between two non-adjacent exons from the same transcript, not found in normal organoids (less than two reads) or in Ensembl, where the first exon is 3 prime to the second exon. Exon reusages were reported only if there were no Ensembl transcripts that would explain the junction between the exons.
ACCESSION NUMBERS The accession numbers for the raw sequence data reported in this paper are The European Genome-phenome Archive under accession numbers EGAD00001002236 and EGAD00001002237 (https://www. ebi. ac. uk/ega/, hosted by the European Bioinformatics Institute [EBI]). project funded by the European Community's Seventh Framework Programme (FP7/2010-2014) under the grant agreement 242006. A. L. and A. -L. B. -D. are supported by Southern and Eastern Norway Regional Health Authority (2011079), Norwegian Cancer Society (0332), and the Norwegian Radium Hospital Research Foundation. We also would like to acknowledge the Core Sequencing Facility and Information Technology groups of the Wellcome Trust Sanger Institute; support for samples, sample banking, and processing from the Breakthrough Breast Cancer Unit; and members of the ICGC Breast Cancer Working Group as well as the Pathology Review subgroup of the Breast Cancer Working Group.
ACKNOWLEDGMENTS This work was supported by the  Wellcome Trust  (grant reference  077012/Z/ 05/Z ). P. J. C. is personally funded through a  Wellcome Trust Senior Clinical Research Fellowship  (grant reference  WT088340MA ). A. S. is supported by an  HL Holmes Award  from the  National Research Council Canada  and an  European Molecular Biology Organization (EMBO)  Fellowship. S. D. , S. M. , and A. L. are funded through the  BASIS  project, which is a European research.
As shown in Figure  2K , the ISRE reporter construct could be activated by CM mafo compared to CM veh. This effect was specific as it was   50  and TGI index concentrations were calculated 3 or 6 days after mafosfamide treatment (results are mean of at least three independent experiments). GI 50 was calculated as the drug concentration that reduced the number of cells to 50% of the number of cells before drug addition. TGI is the drug concentration that achieved total growth inhibition of cells. significantly reduced by addition of the JAK inhibitor AG490 (Figure  2K ) as well as by expression of a siRNA directed against STAT1 (Figure  2L ). Together, these data strongly suggest that breast cancer cells treated with mafosfamide secrete functional cytokines able to trigger paracrine STAT1 signaling, among which type I IFNs appear to be relevant candidates.
We then performed a colony assay using the MCF7 model in order to evaluate whether PARP12 silencing interfered with tumor survival and growth. A very significant delay (~10 days) in cell re-growth (colony formation) was observed when mafosfamide was combined to siNT versus PARP12 siRNA (Figure  6E-6G ). Combining STING and PARP12 siRNA did not exhibit additional or synergistic effect (Supplementary Figure  S6A , S6B), confirming that these two targets belong to the same functional pathway. Taken together, these data indicated that breast cancer cell intrinsic IFN signaling induced by genotoxic stress favors resistance to treatment in a PARP12dependent manner.
PARP14 is involved in genomic stability  [62]. Of note, genetic inhibition of PARP9 and PARP14 was shown to increase chemotherapy efficacy in prostate cancer cells  [63]. PARP12 is much less documented and was shown to be involved in the control of protein translation and inflammation  [58]. This study is the first to suggest its implication in cancer. We may speculate that when combined with mafosfamide, PARP12 inhibition reduces dormant cell survival leading to delayed cancer cell colony regrowth. In agreement, using the Kaplan Meier Plotter database (http://kmplot. com/analysis/), we found that ERpositive breast tumors that overexpressed PARP12 had a worse outcome compared to those expressing low levels of PARP12 (Supplementary Figure  S6C ). Obviously further studies are required to elucidate its mechanism of action in resistance to treatment.
qRT-PCR Total RNA extraction was performed with NucleoSpin RNA XS (Macherey-Nagel) following the manufacturer's instructions. Total RNA (1 µg from each sample) was then reverse transcribed into cDNA with High Capacity cDNA Reverse Transcription Kit (Applied biosystems). Expression of IFN-related genes was analyzed with SYBR Select Master Mix (Life Technologies). qRT-PCR data were invariably normalized to the averaged expression levels of three housekeeping genes: GAPDH (glyceraldehyde 3-phosphate dehydrogenase), RPL13 (ribosomal protein L13) and HPRT1 (hypoxanthine phosphoribosyltransferase 1).
Cytokine array Cytokines expressed by MCF7 cells after mafosfamide (or vehicle) treatment were identified using human cytokine array (panel A from R&D systems, Minneapolis, MN, USA). Three hundred µg of total protein lysates were incubated with the membrane according to the manufacturer's instructions. Horseradish peroxidase substrate (R&D systems, Minneapolis, MN, USA) was used to detect protein expression. Arrays were scanned using Geldoc apparatus (Biorad) and optical density measurement was obtained with the Image J software.
Luciferase assay Luciferase assay was performed in 96-well plate using the Dual-Luciferase Reporter Assay System (Promega) following the manufacturer's instructions. In brief, adherent HEK293 cells were transiently transfected (lipofectamine, Invivogen) with the ISRE (interferon sensitive responder element) luciferase responder plasmid  [66]  and the Renilla plasmid used for normalization. For experiments involving STAT1 silencing, siSTAT1 or siNT were transiently transfected using interferin reagent (Polyplus) 24 h before reporter plasmid transfection. Twenty four hours later, cells were stimulated for 24 h with CM veh or CM mafo , containing or not AG490 (50 µM) or DMSO (vehicle). The luminescence activity in cell lysates was measured using a Mithras LB940 (Berthold) and data were analyzed using the MicroWin2000 software.
Colony assay After 20, 30, 40 or 50 days of mafosfamide and siRNAs treatment, the cells were washed, fixed with 4% PFA 30 min at RT then washed again with PBS. Next, cells were stained with crystal violet (0. 1% crystal violet, 2% ethanol) for 20 min and washed with water. The plate was dried upside-down. Quantification of the area was performed using ImageJ software.
ACKNOWLEDGMENTS The authors would like to thank  Myriam Lassalle ,  Delphine Nicole ,  Sophie Banis ,  Enora LeVen ,  Marie-Emmanuelle Legrier ,  Arnaud Beurdeley  ,  Corinne Cordier ,  Nicolas Goudin  and  Lucila Sackmann Sala , for technical assistance and helpful discussions. The authors also wish to thank  Allan Zwart  for kindly giving the ISRE luciferase reporter plasmid.
0 Fluorometer (Invitrogen, Carlsbad, CA, USA) and quality tested by Agilent 2100 Bioanalyzer High Sensitivity (Agilent Technologies, Santa Clara, CA, USA). RIN values of samples ranged from 7. 3 to 9. 4 (median=9). Libraries were processed with Illumina cBot for cluster generation on the flow cell, and sequenced in paired-end mode on HiSeq2500 (Illumina). Raw data processing was performed using CASAVA v1. 8. 2 of the Illumina pipeline for both format conversion and de-multiplexing. Reads were deposited in the Sequence Read Archive (SRA) under study accession number SRP067564.
(2013). Further analyses were performed on single-mapping reads. A reference annotation-based transcript assembly was constructed (RABT;  Roberts et al. , 2011). Assemblies were performed using Cufflinks v2. 2. 1  (Trapnell et al. , 2010) , which was supplied with the Ptrichocarpa_210_v3. 0. gene_exons. gff3 file. A merged. gtf file was created from the 32 transcripts. gtf output files using Cuffmerge. Based on their mapping, the 41 226 loci were assigned to three categories: unique predicted gene, putative uTAR (unannotated transcriptionally active regions), or chimera (spanning over several predicted genes or tandem repeats). Ambiguous contigs due to assembly artifacts were removed.
Expression analysis and detection of differentially expressed genes Cuffdiff2, supplied with the merged. gtf file, was used with default parameters  (Trapnell et al. , 2012). For each locus, the number of single-mapping reads gave access to a normalized count-based expression that was used to perform a hierarchical clustering of the 32 libraries (Euclidean distance using R package Stats). Mean and normalized expression values were computed as FPKM (fragments per kilobase of transcript per million mapped reads). Eight pairwise comparisons were designed on the basis of their biological relevance (differential expression between zones: EZ-CTL-0. 5h versus DZ-CTL-0. 5h, EZ-CTL-3h versus DZ-CTL-3h, EZ-PEG-0. 5h versus DZ-PEG-0. 5h, and EZ-PEG-3h versus DZ-PEG-3h; stress response: DZ-PEG-0. 5h versus DZ-CTL-0.
Identification, in silico validation, and functional annotation of uTARs The P. trichocarpa genomic sequence at the uTAR location was used as the query for a homology search in plant databases. BLASTN was performed against the expressed sequence tag (EST) database with default parameters and 10 -5 as the maximal e-value (NCBI, http://www. ncbi. nlm. nih. gov), and the best matched poplar EST was reverse-blasted on the P. trichocarpa genome v3. 0 (Suppplementary Table  S3 ). NCBI BLAST+ executable (v2. 2. 8) was used on a local platform against the Non-Redundant Protein Sequence database (April 2014). The command line 'blastx' was executed with default parameters except that the e-value threshold was set to 1. BLAST results and the JBrowse interface were used to discard uTARs mapped on repeats.
Gene annotation, ontology, and enrichment analysis Gene ontology (GO) assignment, GO-Slim summarization, and enrichment analysis were performed using  Blast2Go (v2. 8; Conesa et al. , 2005). The 41 335 P. trichocarpa primary transcripts were blasted against A. thaliana ('blastx', default parameters, except e-value threshold=0. 01). Only the best hit was imported, linking poplar gene models to the GO identifiers of their closest Arabidopsis orthologs. GO was summarized as plant GO-Slim terms. Enrichment analysis consisted of a Fisher's exact test combined with an FDR correction for multiple testing. A corrected P-value of 0. 05 was used to identify enriched GO terms.
Arabidopsis microarrays Three Arabidopsis studies were selected for a pair-wise comparison of DZ and EZ  (Birnbaum et al. , 2003; De Rybel et al. , 2010; Tsukagoshi et al. , 2010). Affymetrix ATH1 Arrays were normalized using R (gcrma 2. 40, default parameters;  Irizarry et al. , 2003). Differential expression was computed for Columbia wild-type as Log2(FC), moderated t-tests implemented in the eBayes function  (limma 3. 24. 12; Smyth, 2004) , and FDR corrections for multiple testing were employed. A zone-preferred expression [corrected P-value <0. 05, Log2(FC)≥2] was detected for 1173, 2972, and 4349 AGI in GSE21876, E-MEXP-2912, and GSE5749, respectively.
Quality of RNA-sequencing raw data The dynamics of transcriptome remodeling under osmotic stress were investigated in the DZ and the EZ using 32 cDNA libraries. RNA-sequencing was performed using Hiseq2500 technology, generating ~30 million pair-end reads of 100 bp per library (Supplementary Table  S1 ). Following quality filtering, reads were mapped onto the P. trichocarpa genome using a reference annotation-based transcript assembly. Only reads that mapped exactly once onto the genome were considered (~95% of mapped reads; Supplementary Table  S1 ). Overlapping reads per locus were counted and normalized using Cuffdiff2 (~13 million counts per library). Transcriptomes were strongly structured according to zone (Fig. 2A ). Within the DZ and EZ clusters, the libraries corresponding to roots collected after 3 h under stress were gathered together, indicating not only the impact of this treatment on the transcriptome but also its reproducibility.
Acknowledgements The research has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under the grant agreement no. FP7-311929  (WATBIO), and from the  French National Research Agency through the Laboratory of Excellence ARBRE  ( ANR-12-LABXARBRE-01 ). MR was supported by  WATBIO  and  Region Lorraine  (no. 12000425 ). The authors thank the facility of Functional Ecology ( PTEF OC081 ) at the research center  INRA Nancy-Lorraine. The authors thank  Cyril Buré  for his daily excellent technical support,  Maryline Harroué  and  Sarah Liéger  for histological analyses,  Allyson Hollander  for qPCR analysis, and  Emmanuelle Morin  for her help in bioinformatics analysis. The authors acknowledge  Marc Villar  and  Catherine Bastien  from the  UR0588-INRA Unit  for access to the referenced Populus nigra clone  6-J29 , and  Olivier Forestier  from  Guéméné-Penfao ONF Nursery  for preparation of the cuttings.