dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was supported by grants from the  MIRMED  project (Genoscope, CNRS) and the  'Laboratoire d'Excellence Saclay Plant Sciences' (SPS ,  ANR-10-LABX-40 , http://www6. inra. fr/saclay-plant-sciences). The laboratories LIPM and LRSV are parts of the  'Laboratoire d'Excellence (LABEX)  entitled  TULIP  ( ANR -10-LABX-41 ). DF PhD was granted by  Agronutrition SA. PB-S was the recipient of a  Marie Curie Postdoctoral IE Fellowship  ( European Commission ,  MEDEPIMIR ,  N°P IEF-GA-2010-273743 ).
Rule 5: Do Not Trust the Defaults Any plotting library or software comes with a set of default settings. When the end-user does not specify anything, these default settings are used to specify size, font, colors, styles, ticks, markers, etc. (Figure  4 ). Virtually any setting can be specified, and you can usually recognize the specific style of each software package (Matlab, Excel, Keynote, etc. ) or library (LaTeX, matplotlib, gnuplot, etc. ) thanks to the choice of these default settings. Since these settings are to be used for virtually any type of plot, they are not fine-tuned for a specific type of plot. In other words, they are good enough for any plot but they are best for none. All plots require at least some manual tuning of the different settings to better express the message, be it for making a precise plot more salient to a broad audience, or to choose the best colormap for the nature of the data.
Rule 10: Get the Right Tool There exist many tools that can make your life easier when creating figures, and knowing a few of them can save you a lot of time. Depending on the type of visual you're trying to create, there is generally a dedicated tool that will do what you're trying to achieve. It is important to understand at this point that the software or library you're using to make a visualization can be different from the software or library you're using to conduct your research and/or analyze your data. You can always export data in order to use it in another tool. Whether drawing a graph, designing a schema of your experiment, or plotting some data, there are open-source tools for you. They're just waiting to be found and used. Below is a small list of open-source tools. Matplotlib is a python plotting library, primarily for 2-D plotting, but with some 3-D support, which produces publicationquality figures in a variety of hardcopy formats and interactive environments across platforms.
It comes with a huge gallery of examples that cover virtually all scientific domains (http://matplotlib. org/ gallery. html). R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc. ) and graphical techniques, and is highly extensible. Inkscape is a professional vector graphics editor. It allows you to design complex figures and can be used, for example, to improve a script-generated figure or to read a PDF file in order to extract figures and transform them any way you like. TikZ and PGF are TeX packages for creating graphics programmatically. TikZ is built on top of PGF and allows you to create sophisticated graphics in a rather intuitive and easy manner, as shown by the Tikz gallery (http://www.
texample. net/tikz/examples/all/). GIMP is the GNU Image Manipulation Program. It is an application for such tasks as photo retouching, image composition, and image authoring. If you need to quickly retouch an image or add some legends or labels, GIMP is the perfect tool. ImageMagick is a software suite to create, edit, compose, or convert bitmap images from the command line. It can be used to quickly convert an image into another format, and the huge script gallery (http://www. fmwconcepts. com/imagema gick/index. php) by Fred Weinhaus will provide virtually any effect you might want to achieve. D3. js (or just D3 for Data-Driven Documents) is a JavaScript library that offers an easy way to create and control interactive data-based graphical forms which run in web browsers, as shown in the gallery at http://github.
com/ mbostock/d3/wiki/Gallery. Cytoscape is a software platform for visualizing complex networks and integrating these with any type of attribute data. If your data or results are very complex, cytoscape may help you alleviate this complexity. Circos was originally designed for visualizing genomic data but can create figures from data in any field. Circos is useful if you have data that describes relationships or multilayered annotations of one or more scales.
Results NELF interacts with the integrator complex subunits. To gain more insight into NELF-mediated RNAPII pausing we immunopurified (IP) NELF and its associated partners from HeLa nuclear extracts. Dignam nuclear extracts prepared from control and Flag-HA-tagged NELF-E subunit (eNELF-E) expressing cells were subjected to tandem affinity chromatography as previously described  16. Flag-HA IPed materials were run on SDSpolyacrylamide gel electrophoresis (SDS-PAGE) and proteins were visualised by silver staining and identified by mass spectrometry (MS) (Fig. 1a  and Supplementary Dataset 1). Major MS-identified eNELF-E nuclear partners are the core subunits of the NELF complex (A, B, C/D). As previously reported, both Spt5 and subunits of RNAPII were recovered  4. Interestingly, MS identified all subunits of the Integrator complex (INTScom) (Fig.
3D ). Of note, in this assay knockdown of INTS11 had no effect on TAR region (Supplementary Fig. 3D ) suggesting a role of INTS11 in regulating transcription elongation at the viral promoter. Taken together, our results revealed an unexpected dual function of the INTScom in regulating RNAPII pausing and processivity at the HIV-1 promoter. INTScom dictates the strength of NELF-mediated RNAPII pausing. We next asked whether the function of the INTScom in regulating RNAPII pausing at the HIV-1 promoter also applies to cellular coding genes. Supporting this idea, microarray analyses showed that INTS3 and INTS11depletion (see Supplementary Fig. 4A  for knockdown efficiency) from HeLa cells led to changes in the expression of thousands of genes (Fig. 3a  and Supplementary Dataset 2a-d). The impact on gene expression could not be attributed to the loss of U1 or U2 snRNA, since no significant changes in their expression were observed within the time frame of these experiments (data not shown) or to an effect on cell cycle (Supplementary Fig.
4B ). Interestingly, a majority of these genes (2,164 genes) were commonly identified as differentially expressed (DE) upon depletion of NELF (Fig. 3a  and Supplementary Dataset 2a). Such significant overlap (P-value o1e À 300) highlighted a novel possible function of INTScom subunits in co-regulating NELF-dependent coding genes. To further investigate whether this involves a direct role of INTScom subunits, ChIP coupled to high-throughput sequencing (ChIP-Seq) was performed using antibodies specific for NELF-E, Spt5, INTS3, INTS11 and H3K4me3. The identified ChIP-Seq peaks of INTS11, like NELF-E, preferentially localised close to the TSSs of coding genes ( þ / À 250 bp) and INTS3 showed similar distributions to Spt5 (Fig. 3b ,c Supplementary Fig. 5A  and Supplementary Dataset 3). Heat maps of the ChIP-Seq reads organised by ranking of genes according to NELF-E levels showed a tight correlation between its binding and the binding of INTScom subunits near TSSs (Fig.
4c ). We next asked whether the impact of INTS11 on RNAPII occupancy might in turn affect RNA levels over its target genes. Inspection of the averaged profile of strand-specific RNASeq ( þ ) reads showed a significant increase along the bodies of upregulated genes upon INTS11-KD (Fig. 4d ; PB1e À 12; Supplementary Fig. 7d ; left box plot and Supplementary Dataset 4). Of interest, the variation in read counts between INTS11-KD and control was barely significant over termination sites (Fig. 4d ; Supplementary Fig. 7d ; PB1e À 2; right box plot). Such 'HIV-like' phenotype was highly enriched among 'direct targets' (genes bound by NELF, Spt5, INTS3 and INTS11) corresponding to a total of 859 genes. Approximately half of these genes (432 genes; PB1e À 191) were further down-regulated upon INTS3-KD in complete agreement with the antagonistic effect of INTS3 and INTS11 (Fig.
5B ). Of interest, TSSs of cellular genes up-regulated upon INTS11-KD were specifically enriched in the HIV-1 TAR/loop motif recently identified as the human NELF Binding Element ('hNBE' 23 ) (Fig. 4e  'CUGGGA' and Supplementary Dataset 5). Accordingly, TSSs harbouring such a motif were highly enriched in the group of genes that were up-regulated upon INTS11-KD (Fig. 4f , P-valueB1e À 48; Supplementary Fig. 8b ), by contrast to down-regulated genes. Our data shows that hNBE/TAR motif is a marker for INTS11 target genes, reinforcing the functional link between INTS11 and NELF in regulating RNAPII pausing at coding genes. Inspection of RNAPII profiles over the termination sites of the 859 'direct targets' of INTS11 (up-regulated genes upon INTS11-KD and with INTScom/NELF peaks in their promoters) showed that they harboured a RNAPII peak within the last 500 bp before termination sites (Fig.
INTScom-mediated U2/snRNA processing depends on the presence of a 3' box ('AAAAACAGACC') upstream of the termination site  14, 24. Importantly, we found a 3' box close to termination sites (o1,500 bp) in more than 1,200 coding genes (see Supplementary Dataset 5). Also, nearly 40% of the genes bound by INTScom and NELF also contained such 3' box near their termination sites (834/2,127). Of the INTScom target genes, only those also harbouring this 3' box showed a clear TESA RNAPII peak, in stark contrast to those with no 3' box near their termination site (Fig. 5c ; compare red and black plots, respectively). As such, these results raised the possibility that, similar to snRNAs, 3'box sequence exacerbates INTScommediated RNAPII processivity defects at a subset of its target coding genes. We therefore asked whether INTS11 endonuclease activity plays a role in regulating RNAPII processivity.
Such analysis showed that INTS11-KD led to a decrease of RNA levels near 3' ends specifically for genes bound by INTScom and NELF (Supplementary Dataset 6A,B) as well as for genes harbouring hNBE/TAR motif near their TSSs (Supplementary Fig. 8D, E ). Of interest, the decrease in RNA levels at termination sites was not detected using mature RNAs (Supplementary Fig. 8F, G ; compare left and right plots/box plots), supporting a role of INTS11 prior to production of mature polyadenylated RNAs, in complete agreement with our data using HIV reporter (Fig. 2 ). Systematic scoring of RNASeq reads near termination sites between INTS11-KD and control, for total RNAs or polyA þ RNAs (see Methods) allowed us to further highlight the role of INTS11 in RNA processivity. The scored defects correspond to genes harbouring decreasing levels of reads near termination sites with respect to reads over gene bodies, similarly to what was found by the NRO.
For one ChIP-Seq experiment chromatin prepared from 100 Â 10 6 HeLa-LTR-Luciferase cells was incubated with 10 mg of antibody (the same protocol was used for all antibodies except for antibodies against histone modifications for which chromatin of only 50 Â 10 6 HeLa-LTR-Luciferase cells was used). Under these conditions the following total amounts of DNA were precipitated: An amount of 10 Libraries were constructed using either Illumina ChIP-Seq DNA Sample Prep Kit (non-multiplexed libraries) or TruSeq ChIP Sample Prep Kit (multiplexed libraries) according to manufacturer's instructions. Image analyses and base calling were performed using the HiSeq Control Software and Real-Time Analysis component (Illumina). Data quality was assessed using fastqc from the Babraham Institute and the software SAV (Sequence Analysis Viewer) (Illumina).
The average of the technical replicates was normalised to RPS14 (see list of primers) levels using the comparative CT method (2 À DDCT). Averages and standard deviations of three experiments are shown in the figures. RNASeq. RNASeq libraries were constructed using the TruSeq stranded mRNA sample preparation kit from Illumina. cDNA libraries of polyadenylated RNAs were generated using 1 mg of whole-cell RNAs following Illumina's instructions. For cDNA libraries of total RNAs whole-cell RNAs were depleted for ribosomal RNAs using the Ribo-Zero Magnetic Gold Kit following manufacturer's instructions. 100 ng of ribosomal RNA depleted whole-cell RNA was used for library construction following Illumina's instructions. Image analysis and base calling were performed using the HiSeq Control Software and Real-Time Analysis component.
Data quality was assessed using fastqc from the Babraham Institute and the Illumina software SAV (Sequence Analysis Viewer). De-multiplexing was performed using Illumina's sequencing analysis software (CASAVA 1. 8. 2). Microarrays. For the preparation of Cy3-and Cy5-labelled aRNA 1 microgram of total RNA/sample was amplified and labelled using the Amino Allyl Message Amp II aRNA Amplification Kit (Ambion; Austin, TX, USA), according to manufacturer's instructions. Labelled aRNAs were added to Hybridisation Buffer, hybridisation component A and alignment oligo (Roche Nimblegen), denaturated at 95 °C for three minutes and applied to an array of a 12 Â 135 K Nimblegen HG18_100718 microarray slide. Hybridisation was carried out at 42 °C for 16 h in hybridisation system 4 (Roche Nimblegen). Hybridised slides were washed according to Nimblegen's protocol.
Microarrays were scanned at 1 mm resolution in both Cy3 and Cy5 channels with Innoscan900 scanner (Innopsys, Carbonne, France) with variable photo multiplier tube settings to obtain maximal signal intensities. Nimblescan v2. 5 software (Roche Nimblegen) was used for feature extraction. Data was stored and visualised using the BASE data management software. Differential expression (DE) was analysed in R using limma (Smyth GK, PMID: 16646809). Genes were considered to be DE if BH adjusted P-value was below 0. 01 ('adj. P. Val', see column 14 of Supplementary Dataset 2B-D). Statistical analyses of ChIP-Seq data. ChIP-Seq peaks of NELF, INTS3 and INTS11 were identified using MACS2 with normalisation to the corresponding input sequenced in parallel. Peaks within promoter regions were then intersected and enrichment tested statistically using Fisher exact test.
ChIP-Seq data for RNAPII upon various depletions were further analysed using a rMAT package by counting normalised (r. p. m. ) ChIP-Seq reads in the indicated windows with respect to TSS ( À 250 to þ 250; þ 500 to þ 1000, þ 500 to the end of genes or from À 500 to 0 of termination sites). RNAPII pausing indices were calculated as previously  10  as a ratio of normalised ChIP-Seq reads on TSSs ( þ / À 250 bp) over that of the corresponding bodies ( þ 500 to þ 1000 from TSSs). The RNAPII processivity index was measured as the log ration of normalised ChIP-Seq reads of RNAPII in gene bodies over termination sites ( À 500 to 0 from TES). To estimate changes in processivity, pair-wise wilcoxon tests were performed between the same set of genes, in two conditions (for example, INTS11-KD and control).
Proportional Venn diagrams were plotted using http://bioinforx. com. Statistical analyses of ChIP-Seq by principal component analysis (PCA) were performed using the package FactoMiner from R by taking the total (normalised) read counts as measured from our ChIP-Seq data within windows corresponding to the indicated regions. The generated averaged values were then provided for each ChIP-Seq and for each individual gene allowing to perform clustering ascendant hierarchical for all centred and normalised data using the package FactoMiner from R. Clustering ascendant hierarchical measures Ward distances reflecting the minimal variance among all data sets provided  33  , which was performed separately for genes harbouring NELF, Spt5, INTS11 and INTS3 or not. For comparison of data among different sets of genes (for example, Fig.
3h  according to binding of NELF/INTS11/INTS3), a simple Wilcoxon test was used. For comparison of mockdepleted (scrambled) control with INTS3-or INTS11-depleted samples, a pair-wise Wilcoxon test was performed between each condition and for every group of genes defined according to the presence or not of NELF, INTS3 and/or INTS11 peaks as indicated. Motif scan was performed by systematically recording of the motifs found in each group of ChIP-Seq peaks (INTS3/11 and NELF) using RSAT-tools  34  (www. rsat. ulb. ac. be/rsat/) and the JASPAR database. The relevance of the identified motifs was then tested for enrichment by intersection analysis with a list of INTS11 and/or NELF ChIP-Seq peaks through fisher exact test (see Supplementary Fig. 6a ). Only the motifs with a significant overlap with ChIP-Seq peaks (Po1e-3) were further analysed for their enrichment in up-or down-regulated genes (Fig.
4 ) or in genes harbouring processivity defects upon INTS11-KD (Fig. 5 ). A complete list of all motifs is provided in Supplementary Dataset 5. Fisher exact tests were performed to test which of these motifs (versus no motif) was specifically enriched among the indicated group of up-/down-regulated genes upon INTS11-KD and/or NELF-KD, and for RNASeq À / þ reads. Statistical analyses of gene expression data. Gene expression analyses were performed through both microarrays and strand-specific RNASeq analyses from three independent pools of cells efficiently depleted of INTS3, INTS11 or NELF as compared with mock-depleted control cells. Briefly, following the alignment of sequence reads on the human genome version hg19 with TopHat 2. 0. 9, all subsequent analyses were done in R 3. 0. 2 (www. r-project.
org) with Bioconductor packages (www. bioconductor. org), including GenomicRanges 1. 14. 4 (ref. 35), Rsamtools 1. 14. 3 and Homo. sapiens 1. 1. 2. Coverage data for each gene (exons only) was calculated from aligned RNASeq reads. For each sample, the processivity index was calculated as the ratio of coverage for the (TES-500 bp, TES) region to the (TSS þ 500 bp, TSS þ 1000 bp) region. Differential processivity in total and polyA þ RNASeq data was assessed using a moderated Student's t-statistic as implemented in the limma Bioconductor package  36. Raw P-values were adjusted for multiple testing using the q-value package  37. Genes with various q-valueo1, 5 or 20% were used to test the reproducibility of the measure of differential processivity from triplicates and then between INTS11-KD and control samples.
For heat maps, ChIP-Seq peaks identified using MACS2 were intersected with all TSSs followed by statistical test for enrichment in DE gene lists (Fisher exact test; Fig. 3 ). Log P-values were obtained after applying the Hochberg & Benjamini corrections. RNASeq analyses (RNASeq; Hi-Seq2500; Illumina) were performed from three independent replicates for each condition. Alignments were performed using the Burrows-Wheeler alignment tool software on genome annotations of release H19 of human database for parsing and the EdgeR package for identification of the DE genes (up-or down-regulated; P-valueo0. 001) from the three independent replicates. The specificity of the lists of DE genes was functionally verified through other packages (for example, DESeq2) of through manual read counting with HTseq.
01; see Supplementary Dataset 2B-D; see Methods). The enrichment between these two lists is indicated in Log P-value (see colour bar). (e) Averaged RNA RNAPII profiles as measured by ChIP-Seq as a function of NELF-E-, Spt5-, INTS3 and INTS11 binding. The ChIP-Seq profiles of 'High/Low pausing' are indicated for comparison (RNAPII profiles of highly paused (blue line)-or active genes (light blue line), respectively, as previously described 22 ). Genes bound by À NELF, SPT5 and INTS3 with or without INTS11 (purple and orange line, respectively) or by À NELF and SPT5 with or without INTS3 (yellow and orange line, respectively) or by SPT5, INTS3 and INTS11 (green line). (f) Box plots showing the RNAPII pausing indices depending on NELF/SPT5/INTS3 and INTS11 binding or not ( þ / À ) to the corresponding TSS.
P-values, wilcoxon pair-wise tests. 68/32%, percentages of genes up-regulated upon INTS11-KD that were also up-regulated upon NELF-KD (68%) or not (32%)(see Fig. 3a; Supplementary Dataset 2 for a list). (d) Average profiles of RNASeq ' þ ' reads in control-, INTS11 À or NELF-E-depleted cells over exons (left) or transcription ends (right) for 'direct targets' (genes bound by NELF-E/INTS3/INTS11) and that were upregulated upon INTS11-KD as compared to control. Y axis, normalised ChIP-Seq reads (see Methods). (e) Scatter plot showing the percentage of INTS11 (x axis) and of NELF (y axis) ChIP-Seq peaks harbouring a given consensus motif. Binding motifs were obtained through systematic search among all ChIP-Seq peaks of INTS11 and NELF using RSAT (see Methods). Only the motifs showing significant intersections with INTS11 or NELF peaks (fisher exact testo1e À 3) were analysed (see Supplementary Dataset 5 for a list).
(f) Scatter plot showing the enrichment of genes harbouring a given motif ( þ / À 250 bp from TSSs; see Methods) among DE genes (up-or down-regulated upon INTS11-KD; x axis and y axis, respectively; in Log2 P-values). Note that TAR motif are enriched among up-regulated genes (see Supplementary Dataset 5 for a list of all motifs).
The red and blue dots correspond to the scores obtained for the subset of genes harbouring TAR or 3' box (see Supplementary Dataset 5). (f) Averaged RNASeq þ reads in knockdown or control cells (as indicated) near TES (x axis) of genes bound by INTScom harbouring a 3' box (red plot; 41,200 genes; see Supplementary Dataset 5) or not ('control' genes) near their TES.
Acknowledgements We thank members of the  Molecular Virology lab  for critical reading of the manuscript. ChIP-Seq and RNASeq experiments were performed using MGX-Montpellier facilities. This work was supported by grants from the  ERC  ( 250333 ),  Sidaction ,  ANRS  and  FP7 'HIT HIDDEN HIV ' to M. B. and by grants from the  ANR 'INSULa'  and the  ATIP-Avenir joint program  of the CNRS and  Inserm  to OC. BS was supported by  ERC  ' Silent HIV'  fellowship.
Microarray analysis The microarray hybridizations were done for 36 samples (three pools of graft interface zones of CS/RG, CS/1103P, and CS/CS harvested 3, 7, 14, and 28 d after grafting) by the Plateforme Biopuces, Institut National des Sciences Appliquées, Toulouse, France according to the manufacturer's instructions. The microarrays used were the grape whole-genome microarrays from Nimblegen, Roche (design name 090918 Vitus exp HX12) and were background corrected, quantile-normalized, and summarized as described by  Cookson et al. (2013). The raw and normalized microarray data is available at http://www. ebi. ac. uk/arrayexpress (last accessed 20 March 2014) (accession number E-MTAB-1610). The genes differentially expressed between the scion/rootstock combinations at 3-7, 7-14, and 14-28 d after grafting were identified using limma  (Smyth, 2005 ; log 2 fold-change >1, P<0.
, 2011) ; -validation of satellite observations. In this paper, we give an overview of the Support to Aviation Control Service (SACS) which deals with the first two points listed above as it is an automated global system for the monitoring and warning of volcanic plumes. It makes use of NRT ash and SO 2 data products from satellite instruments operating in the ultraviolet (UV)-visible (data from the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experiment-2 (GOME-2) on-board the two polar orbiting meteorological satellites (MetOp-A and MetOp-B) operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)) and operating in the thermal infrared (IR) (data from the Atmospheric InfraRed Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI) on-board MetOp-A and MetOp-B) wavelength ranges.