dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, indoor exposure was only assessed from modeling and not adjusted with existing measurement data. For a better characterization, Cartoradio indoor measurement data will be integrated to spatially correlate model outputs with measurements and rescale estimates using geostatistical methods such as kriging with external drift. The larger contribution of mobile phones compared to indoor sources in previous studies can reflect the lower exposure from 3G compared to that from 2G mobile phones. It could again be the case with 4G phones that the phone contribution comes back to be larger than in this study. On the other hand, we have made simplified modeling assumptions regarding the building penetration loss. For a realistic estimate, one should consider the influence of building materials in Rf-Emf propagation instead of fixing a deterministic value.
The main mechanisms involving lncRNAs in AS modulation have been classified as follows: (i) lncRNAs interacting with splicing factors  [24] [25] [26] [27] [28] ; (ii) lncRNAs forming RNA-RNA duplexes with pre-mRNA molecules  [29, 30] ; and (iii) lncRNAs affecting chromatin remodeling of alternatively spliced target genes  [31, 32]. In Arabidopsis thaliana, the lncRNA ASCO (ALTERNATIVE SPLI-CING COMPETITOR; AT1G67105) is recognized in vivo by the plantspecific NUCLEAR SPECKLE RNA-BINDING PROTEINS (NSRs), involved in splicing  [24]. Interestingly, there is no evidence that ASCO undergoes splicing, although it is recognized by splicing factors. The analysis of a transcriptomic dataset of the nsra/b mutant compared to wild-type (WT) plants revealed an important number of intron retention events and differential 5 0 start or 3 0 end in a subset of genes, notably in response to auxin  [33].
ASCO modulates the alternative splicing of a subset of pathogen-related mRNAs with unaltered accumulation Considering that overexpression of ASCO affected the AS of NSR mRNA targets  [24] , we searched for mis-spliced genes potentially explaining the global physiological impact of ASCO deregulation. To this end, we used two complementary approaches to detect both differential AS based on annotated isoforms (Reference Transcript Dataset for A. thaliana, AtRTD2)  [52]  and potentially nonannotated differential RNA processing events using RNAprof  [33, 52]. Based on RNAprof, a total of 303 differential RNA processing events in 281 distinct genes were identified comparing RNAi-ASCO with WT plants in control growth conditions (Dataset EV2), whereas the SUPPA2 method  [53]  identified 205 genes with evidence of differential AS in the AtRTD2 database.
Analysis of differential AS events with SUPPA revealed 317 significant DAS events (|dPSI| > 0. 1, P < 0. 01) on 205 unique genes from the AtRTD2 transcript annotation database (Dataset EV3). Similarly to the analysis with RNAprof, most of these events corresponded to intron retention (62%) but we also identified a significant number of alternative 3 0 splice site and alternative 5 0 splice site selection modulated in ASCO knock-down lines (Fig 2C). To determine the most significant impact of the AS events, we sought to identify isoform switching events (i. e. , co-ordinated variations in abundance of two isoforms) using the IsoformSwitchA-nalyzeR package (Dataset EV4)  [54]. Strikingly, isoform switching events were detected for 52 genes, out of which 12 and 34 were common cases detected by RNAprof and AtRTD2-SUPPA, respectively (Fig  2D ).
In silico analysis of the protein sequences derived from switching isoforms indicated that the AS events may lead to (i) change of ORF length, (ii) gain or loss of conserved PFAM protein domain and signal peptides, and (iii) change of the coding potential and the sensitivity to NMD (Fig  2E ). Since AS can often trigger NMD, an important mechanism of plant gene expression regulation  [55] , we compared DAS genes to those transcripts overaccumulated in the double mutant of the NMD factor homologs UP FRAMESHIFT1 (UPF1) and UPF3, upf1-upf3  [56]. As shown in Fig 2F, 66 and 29 genes regulated by NMD were reported by RNAprof and AtRTD2-SUPPA as alternatively spliced in RNAi-ASCO plants, respectively. Hence, the majority of AS events controlled by ASCO seem to be independent of the UPF1-UPF3-mediated RNA quality control machinery, at least in the conditions previously assessed.
The splicing of the SR34 own pre-mRNA is auto-regulated and depends on the activity of immune response factors  [60]. Other relevant AS targets are SNC4 (AT1G66980) which encodes a receptor-like kinase that participates in the activation of the defense response, and its AS is impaired in defense-related mutants, affecting the response to pathogens  [60] ; SEN1 (AT4G35770), a senescence marker gene A Comparison of differentially processed transcripts (RNAprof) and differential AS genes (AtRTD2-SUPPA) with differentially expressed genes (DEG). B Number of genes containing at least one differential RNA processing event (as defined by RNAprof P adj < 0. 001) in CDS, introns, 5 0 UTR and 3 0 UTR between RNAi-ASCO1, and WT. Up and down fractions correspond to increase or decrease, respectively, of RNA-seq coverage in RNAi-ASCO1 for each specified gene feature.
C Proportion of DAS events identified by AtRTD2-SUPPA in RNAi-ASCO1 compared to WT; alternative 3 0 site (A3 0 ), alternative 5 0 site (A5 0 ), intron retention (IR), exon skipping (ES). D Comparison of differentially processed transcripts (RNAprof) and differentially AS genes (AtRTD2-SUPPA) with genes showing significant isoform switch events (Isoswitch). E Summary of the predicted consequence of the isoform switch events as shown by the feature acquired by the upregulated isoform. ncRNA stands for noncoding RNA, and NMD stands for nonsense-mediated decay. F Comparison of differentially processed transcripts (RNAprof), differentially AS genes (AtRTD2-SUPPA), and differentially expressed genes (DEG) with genes significantly upregulated in the upf1-upf3 mutant  [56] , indicating genes potentially regulated by NMD.
The fold enrichment was calculated between ODD or EVEN samples against LacZ. These samples were used for protein precipitation and mass spectrometry analyses (ChIRP-MS), from which PRP8a was identified as a potential ASCO partner. B Validation of PRP8a-ASCO interaction by PRP8a-RIP. U5 RNA was used as a positive control. C Immunoblot analysis was performed during PRP8a immunoprecipitation (IP). The same volume of input, unbound fraction was loaded as well as 20% of the eluted IP fraction. a-PRP8a and a-IgG : IP performed with anti-PRP8a or control rabbit IgG antibody, respectively. D PRP8a recognition of a subset of DAS RNAs is impaired in the 35S:ASCO plants. A housekeeping gene (PP2A, AT1G13320) RNA was used as a negative control. Data information: In (B and D), the results are expressed as a percentage of the input for PRP8a RIP followed by RT-qPCR, and IgG RIP was used as a negative control.
Error bars represent the standard error of 3 biological replicates. N/D stands for nondetectable. The asterisk (*) indicates a significant difference as determined by Student's t-test (P < 0. 05, n = 3). used in independent sets called EVEN and ODD, respectively (Dataset EV5). ASCO-ChiRP was performed in 4 biological replicates for each set of probes: ODD, EVEN, and an additional set matching the LacZ RNA, used as a negative control. ASCO enrichment was corroborated by ChiRP followed by RNA purification and RT-qPCR (Fig  5A ). We then performed a mass spectrometry on the proteins from the purified ASCO-containing RNP to identify potential ASCO protein partners. Strikingly, among the RNA-related proteins identified in the EVEN and ODD samples, but not in the LacZ, we found the pre-mRNA-processing-splicing factor 8A, PRP8a.
Generation of transgenic lines ProASCO::GUS transgenic lines The promoter region of ASCO (2631-bp upstream of the transcription start) was amplified from A. thaliana genomic DNA using gene-specific primers listed in Dataset EV5. The amplicon was subcloned into the pENTR/D-TOPO vector and recombined in a pKGWFS7 binary destination vector, upstream of the GFP, and GUS sequences. ProASCO::GUS constructs were transferred into A. thaliana by standard Agrobacterium-mediated protocol  [100]. Three lines were selected based on 3:1 segregation for the transgene (single insertion) and brought to T3 generation where the transgene was in a homozygous state. All lines behave similarly as for GUS expression.
RNAi-ASCO knocked-down lines The first 233-bp of the ASCO transcript was amplified from A. thaliana genomic DNA using gene-specific primers listed in Dataset EV5. Amplicons were subcloned into the pENTR/D-TOPO vector and recombined in a pFRN binary destination vector  [101]  to target the ASCO RNA by long dsRNA hairpin formation.
Root growth analysis For analysis of auxin impact on root architecture, plants were grown as described in  [Ref. 24]. Briefly, seeds were sterilized and directly sown on plates containing 1/2MS medium supplemented or not with 100 nM NAA. Plantlet root architecture was analyzed using the RootNav software after 7 days of growth  [102]. For analysis of flagellin impact on root architecture, plants were previously grown 5 days on solid 1/2MS medium + 1% sucrose and then transferred for additional 9 days in liquid 1/2MS media + 1% sucrose supplemented or not with 0. 1 lM or 1 lM of synthetic flg22 peptide (GeneCust). For each plantlet, lateral roots were counted, and the primary root length was measured using the RootNav software. Experiments were done at least two times, with a minimum of 16 plants per genotype and condition.
05, n ≥ 3 biological replicates). All the used primers are listed in Dataset EV5. For RT-PCR analysis, the amplification was performed using Phusion High-Fidelity DNA Polymerase and transcript-specific primers as manufacturer's protocol. PCR products were separated on an 8% polyacrylamide gel stained with SYBR Gold (Thermo Fischer Scientific) and revealed using a ChemiDoc MP Imaging System (Bio-Rad). Band intensity was quantified using ImageJ package (https://imagej. nih. gov/ij/). Isoform ratio was calculated as the ratio of intensity of the two bands corresponding either to the alternatively spliced or to spliced transcript isoforms, respectively.
RNA-seq preprocessing included trimming library adapters and quality controls with Trimmomatic  [106]. Paired-end reads with Phred Quality Score Qscore > 20 and read length > 30 bases were kept, and ribosomal RNA sequences were removed with SortMeRNA  [107]. Processed reads were aligned using Tophat2 with the following arguments: -max-multihits 1 -i 20 -min-segment-intron 20 -min-coverage-intron 20 -library-type frfirststrand -microexon-search -I 1,000 -max-segment-intron 1,000max-coverage-intron 1,000 -b2-very-sensitive. Reads overlapping exons per genes were counted using the FeatureCounts function of the Rsubreads package using the GTF annotation files from the Araport11 repository (https://www. araport. org/downloads/ Araport11_Release_201606/annotation/Araport11_GFF3_genes_tra nsposons.
AS analysis The RNA profile analysis was performed using the RNAprof software (v1. 2. 6) according to  [Ref. 33]. Briefly, RNAprof software allows detection of differential RNA processing events from the comparison of nucleotide level RNA-seq coverage normalized for change in gene expression between conditions. Here, the RNAprof analysis compared RNA-seq data from biological triplicates of WT, RNAi-ASCO1, and 35S:ASCO1 lines. Differentially processed regions genes were filtered as follows: fold change > 2 and P < 0. 001. Overlap between gene features and differentially processed regions was done using in-house R scripts (https://github. com/JBazinIPS2/Bioinfo/blob/master/RN Aprof_events_selection. Rrst). Only regions fully included in a gene features were kept for further analysis. The RNAprof software archive, including documentation and test sets, is available at the following address: http://rna.
igmors. u-psud. fr/Software/rnaprof. php. Transcript level quantification was performed using pseudo-alignment counts with kallisto  [109]  on AtRTD2 transcripts sequences (https://ic s. hutton. ac. uk/atRTD/RTD2/AtRTDv2_QUASI_19April2016. fa) with a K-mer size of 31-nt. Differential AS events in the AtRTD2 database were detected using SUPPA2 with default parameters  [53]. Only events with an adjusted P < 0. 01 were kept for further analysis. Isoforms switch identification was performed with the IsoformSwitchAnalyzeR package  [54]  according to  [34].
Liquid Chromatography-Mass Spectrometry (LC-MS) analysis Proteins purified from ribonucleoprotein complexes were analyzed using the King Abdullah University of Science and Technology proteomic facilities. Dry pellets of samples purified with either ODD, EVEN, or LacZ probe sets were solubilized in trypsin buffer (Promega) for digestion into small peptides. The solubilized peptides were then injected into a Q Exactive TM HF hybrid quadrupole-Orbitrap mass spectrometer (Thermo Scientific) with a Liquid Chromatography (LC) Acclaim PepMap C18 column (25 cm length × 75 lm I. D. × 3 lm particle size, 100 A ˚porosity, Dionex). Data were analyzed for each sample using Mascot software (Matrix Science), with a minimal sensitivity of 2 detected peptides per identified protein.
4, 50 mM NaCl, and 10 mM EDTA). Twenty microliters were saved for further immunoblot analysis. After Proteinase K (Ambion AM2546) treatment, beads were removed with a magnet, and the supernatants were transferred to a 2-ml tube. RNA was extracted using TRI Reagent (Sigma-Aldrich) as indicated by the manufacturer. Eighty microliters of nuclei extracts was used for input RNA extraction. The immunoprecipitation and input samples were treated with DNase, and random hexamers were used for subsequent RT. Quantitative real-time PCR reactions were performed using specific primers (Dataset EV5). Results were expressed as a percentage of cDNA detected after immunoprecipitation, taking the input sample as 100%. For Western blot analysis, immunoprecipitated proteins were eluted by incubating 20 ll of beads in 20 ll of 2× SDS-loading buffer without b-mercaptoethanol (100 mM Tris-HCl pH 6.
Acknowledgements Synthetic flg22 peptide was kindly provided by  J. Colcombet  ( IPS2 ). Seeds from the prp8-7 [35,113] and the smd1b [71] mutants were kindly provided by  H. Vaucheret. We thank  Wil Prall  for critical reading of the manuscript. This work was supported by grants from  ANPCyT  ( PICT 2016-0289  and -0007, Argentina),  CNRS  ( Laboratoire International Associé NOCOSYM ), the  "Laboratoire d'Excellence (LABEX) "  Saclay Plant Sciences (SPS ;  ANR-10-LABX-40 ), the  Ministère Français de l'Enseignement Supérieur et de la Recherche (RR) , and the  ANR  grants ( ANR-15-CE20-0002-01   EPISYM  and  ANR 16-CE20-0003-04 SPLISIL ) as well as the  EPIMMUNITY International  project between IPS2, France and  KAUST University, Saudi Arabia. The POPS platform benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ).
Data availability Data were deposited in CATdb database  [114]  (http://tools. ips2. u-psud. fr/CATdb/) with ProjectID NGS2016-07-ASCOncRNA. This project was submitted from CATdb into the international repository GEO (http://www. ncbi. nlm. nih. gov/geo) with ProjetID GSE135376. Expanded View for this article is available online.
Fluorescent reporter analyses Analysis of nlp-29p::GFP expression was quantified with the COPAS Biosort (Union Biometrica; Holliston, MA) as described in  Pujol et al. (2008b). In each case, the results are representative of at least three independent experiments with more than 70 worms analysed. The ratio between GFP intensity and size (time of flight; TOF) is represented in arbitrary units. Statistical significance was determined using a non-parametric analysis of variance with a Dunn's test (Graphpad Prism). Fluorescent images were taken of transgenic worms mounted on a 2% agarose pad on a glass slide anesthetized with 0. 01% levamisole, using the Zeiss AxioCam HR digital colour camera and AxioVision Rel. 4. 6 software (Carl Zeiss AG).
Image acquisition and laser wound Since in preliminary experiments we observed that immobilization using latex beads impacted vesicle and protein dynamics in the epidermis, young adult worms (containing less than five eggs) were immobilized on a slide using 0. 01% levamisole. Time lapse and/or Z stack were acquired using two different spinning disk microscopes: an inverted Visitron Systems GmbH spinning disk with a Nikon 40X oil 1. 3 NA objective and 1. 5X lens, and a Roper inverted spinning disk with a Nikon 100X oil 1. 4 NA objective, were used with their respective acquisition software (VisiView or Metamorph). For spatially resolved time-lapse imaging, a Zeiss LSM780 confocal microscope whose GaAsP detector was chosen for its high sensitivity and controlled through the Zen acquisition software.
Image analysis All image processings were done using Fiji software. A custom Image J macro was used to correct for Z-drift, while XY drift was corrected either with the Image Stabilizer or the Correct 3D drift plugin from ImageJ or the online available macro 'NMSchneider/FixTranslation'. To analyse EB1 density before wounding, we manually counted fluorescent dots as described in  Chuang et al. (2016) , in a 300 mm 2 ROI in the lateral hyp7 or in an 80 mm 2 ROI in the seam cell in five worms per RNAi condition. Similarly, we measured SNF-12 cluster density in the lateral hyp7, analysing six worms per RNAi condition. Quantification of EB1 recruitment at the wound site was performed by measuring the RawIntDen (sum of the pixel intensities) of a selected ROI of 256 mm 2 for EB1 centred at the wound site.
We then defined particles using the Trackmate plugin  (Tinevez et al. , 2017) , with estimated blob diameter of 3 pixels, and a threshold at 900, linking distance and gap-closing max distance two pixels, gap-closing max frame gap of 1. From the resulting file, the track ID and x,y positions were analysed using a custom R script that calculated vectors relative to the wound site for each moving particle. To compare the actin ring closure upon different RNAi condition, a ROI of 80 pixels 2 located on the centre of the wound was selected 15 min post-wounding. The area inside the actin ring was detected using the Huang automatic threshold method from Image J. To first compare the recruitment of EB1 versus actin, the radial profile (plugin from Paul Baggethun) was calculated, after determination of the centroid of the wound for each time point by manually selecting the wound area.
, 2012). We then estimated the angle q between the local direction of microtubule bundle and the direction to a point of reference (the centre of the wound region); q lies in the 0 to +90 sector since it measures the discrepancy between two directions. We then measured the averaged angle q over three annular sectors labelled according to their distance to the centre of the wound: R1, R2, R3. Statistics were performed in Matlab(R) using the circular Statistic toolbox  (Berens, 2009). Custom R and Matlab scripts are either available as source file or upon request.
Acknowledgements We thank  Annie Bonnet ,  Vincent Rouger ,  Guillaume Bordet ,  Sham Tlili ,  Claudio Collinet  and  Pierre Mangeol  for their contributions,  Asako Sugimoto ,  Andrew Chisholm ,  Michel Labouesse ,  Sander van den Heuvel ,  Michalis Barkoulas ,  Bart Grant ,  Xiaochen Wang ,  Jessica Feldman ,  Reto Gassmann  and the  Caenorhabditis Genetics Center  ( University of Minnesota, Minneapolis, MN ) supported by the  National Institutes of Health Office of Research Infrastructure Programs  ( P40 OD010440 ) for strains,  Ari Pani ,  Anne Cecile Rayman ,  Gregoire Michaux  and  Michael Sixt  for plasmids,  Chris Crocker  at  Wormatlas  (supported by  NIH   OD010943  to  David Hall ) for diagrams, and  Didier Marguet ,  Ariane Abrieu ,  Alphee Michelot ,  Thomas Lecuit  and members of our lab for discussions and critical reading of the manuscript.
Worm sorting was performed by  Jerome Belougne  using the facilities of the  French National Functional Genomics platform , supported by the  GIS IBiSA and Labex INFORM. We thank the  imaging core facility (ImagImm) of the Centre d'Immunologie de Marseille-Luminy (CIML)  supported by the  French National Research Agency program  ( France-BioImaging   ANR-10-INBS-04 ), and  Centuri  funded by the  « Investissements d'Avenir » French Government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and from  Excellence Initiative of Aix-Marseille University  -A*MIDEX.
Hence, we used the same Bonferroni threshold as in Section 2. 2 to determine the significant SNPs in BCAC. We also computed gene-scores in the BCAC data using VEGAS2, as in Section 2. 1. In this case, we did use the summary statistics of all 13 250 641 available SNPs and the genotypes from European samples from the 1000 Genomes Project  [43]  to compute the LD patterns. Since these genotypes did not include chromosome X, we excluded it from this analysis. All comparisons included only the genes in common between GENESIS and BCAC, so we used a different Bonferroni threshold (1. 66 × 10 -6 ) to call gene significance.
Implementation details We parsed and cleaned the input text using some basic NLTK  [5]  functions with Python 3. For the construction of graphs-of-words we re-used a code provided by a colleague, S. Khalife. We used the k-means implementation in scikit-learn  [41]. Graph realizations were obtained by solving the optimization problems in Eq. (  3 ) and (  4 ) with off-the-shelf solvers Mosek  [39]  and IpOpt  [11]. We implemented the ANN using the keras python library  [10]. The default configuration was chosen for all layers. We used the Adam solver  [21]  in order to solve the training problem Eq. (  8 ). For each training set we tested, 35% was used for training, 35% for validation, and 30% for testing.
Image processing 1. Download Fiji  19  from the ImageJ website (https:// imagej. net/Fiji). An ImageJ macro code has been provided which automatically does the image analysis steps described below and can also be changed to suit the application. The individual steps of the macro are described in steps 5. 3-5. 5. 12. Convert the values from number of pixels to area (in this case µm  2  ) by multiplying by the pixel size squared. Calculate the size of each pixel by imaging a graticule with the high-speed camera at the same magnification as was used for imaging and use ImageJ to set the scale. 13. Plot the data. It is also possible to conduct statistical analysis to show any significant difference in the area of bubbles if comparing different conditions.
The Arabidopsis DAO1 protein sequence was used as a query in BLASTP searches for predicted proteins with high confidence from P. abies and P. taeda genomes (P. abies and P. taeda v. 1. 0, http://congenie. org/; Nystedt et al. , 2013; Zimin et al. , 2014). AtDAO1 protein was used to blast embryophyte and chlorophyte genomes in PHYTOZOME, allowing the selection of putative 2OGD protein sequences from S. moellendorffii, P. patens, Coccomyxa subellipsoidea, Micromonas pusilla, Ostreococcus lucimarinus, Volvox carteri, Dunaliella salina and Chlamydomonas reinhardtii. The DAO protein sequences of O. sativa, P. trichocarpa and M. domestica were retrieved from PHYTOZOME and GenBank (National Center for Biotechnology Information; https://www. ncbi. nlm. nih. gov/genbank/). Multiple sequence alignment was performed using MUSCLE  (Edgar, 2004)  as implemented in MEGA6 (http://megasoftware.