dataesr/scientific-paragraphs-categorization huggingface dataset:
5 embryos) and with the NEXTflex ChIP-Seq Kit (Bioo Scientific, E9. 5 embryos). ChIP Seq experiments involved biological duplicates. Sequencing was performed on multiplexed samples using 50 bp single-end reads on an Illumina HiSeq system (E9. 5 embryos) or using 42 bp paired-end reads on an Illumina NextSeq (E8. 5 embryos) according to the manufacturer's specifications. Chip-seq data were analysed as follows, using Eoulsan  [52]  with extended support for ChIP-seq workflows (https://github. com/GenomicParisCentre/eoulsan/tree/branch-chip-seq). First, reads were filtered out when they would not pass Illumina filters (module filterreads with option illuminaid). Files corresponding to technical replicates were merged (module technicalreplicatemerger, with option format = fastq), followed by trimming of the reads using Trim Galore! (http://www.
bioinformatics. babraham. ac. uk/projects/trim_galore/; version 0. 4. 1 in module trimadapt, with cutadapt v1. 8. 1 and options: length = 41, quality = 20, error = 0. 1, stringency = 8, and is. paired = yes for E8. 5 data and is. paired = no for E9. 5 data). Mapping was performed using STAR  [53]  (version 2. 4. 0k in module mapreads with mapper. arguments = "-outFilterMultimapNmax 1-outFilterMis-matchNmax 999-outFilterMismatchNoverLmax 0. 06-alignIntronMax 1-alignEndsType EndToEnd-alignMatesGapMax 2000-outSAMunmapped Within"). Further filters were applied to the mapped reads before conversion into BAM (module filtersam with removeunmapped = true; module sortsam; module rmdupgalax with is_sort = true; module sam2bam with compression. level = 5). BIGWIG files were created from the resulting BAM files using deep-Tools' bamCoverage  [54]  (version 1.
0. 20140616), using a q-value< = 0. 01 threshold (other parameters as default). For quantification, we first defined a set of non-redundant enriched regions for all samples by taking the union of all peak summits from both replicates of all samples, grouped together all summits distant from less than 50 bp, and for each group kept only the summit with the lowest q-value (calculated by MACS2). We then quantified the signal at all summits in each sample by counting the number of fragments (using the R bioconductor package csaw, v. 1. 0. 7). Normalisation and statistical analysis were performed using the bioconductor DESeq2 package (1. 6. 3). Library size factors were calculated on fragment counts in genomic bins of 10 kb. Comparison between wild type, Krox20 ΔC/ΔC and Krox20 ΔA/ΔA embryos was performed using negative binomial Wald Test (DESeq2).
Acknowledgments We are grateful to Benoı ˆt Noe ¨l for help with the UCSC trackhubs, to  Melody Matelot  for help with the 4C-experiments and to  Michel Wassef  for critical comments on the manuscript and we thank the genomic platform of the IBENS. 4C-seq data analysis was performed at the  Vital-IT  (http://www. vital-it. ch)computing center of the  Swiss Institute of Bioinformatics  using tools developed by the  BBCF  (http://bbcf. epfl. ch).
Funding: The P. C. laboratory was financed by the  Institut National de la Recherche Medicale , the  Centre National de la Recherche Scientifique , the  Ministère de la Recherche et Technologie  and the  Fondation pour la Recherche Medicale (FRM). It has received support under the  PIA  launched by the  French Government  and implemented by the  ANR , with the references:  ANR-10-LABX-54  of the feedback loop. its accessibility and its activity. Our work uncovers a novel, longrange mode of cooperation between enhancers that restricts the domain of action of autoregulatory enhancers within embryos and might be essential to avoid their inappropriate activation. MEMOLIFE  and  ANR-11-IDEX-0001-02   PSL Ã Research University. The genomic platform of the IBENS is supported by  France Ge  ´nomique national infrastructure and funded as part of the  Programme "Investissements d'Avenir" (PIA)  managed by the  Agence Nationale de la Recherche  ( ANR , contract  ANR-10-INBS-0009 ).
, 2011)  or has been limited to intra-sentential relations  (Maziero et al. , 2015). Moreover, all discourse corpora are limited in size, since annotation is complex and time consuming. This data sparsity makes learning hard, especially considering that discourse parsing involves several complex and interacting factors, ranging from syntax and semantics, to pragmat-arXiv:1701. 02946v1 [cs. CL] 11 Jan 2017 ics. We thus propose to harmonize existing corpora in order to leverage information by combining datasets in different languages. Contributions In this paper, we propose a new discourse parser that is significantly better than existing parsers for English on 2/3 standard metrics. Our parser relies on fewer features than previous work and is arguably algorithmically simpler. Moreover, we present the first end-to-end statistical discourse parsers for other languages than English (6 languages, in total).
Harmonization of the datasets Recent discourse parsers built on the En-DT are based on pre-processed data: the corpus contains only binary trees, with the large label set mapped to 18 coarse-grained classes. In this section, we describe this pre-processing step for all corpora used. Discourse corpora have been released under three different file formats: dis (En-DT), lisp (Rhetalho and CorpusTCC) and rs3 (all remaining corpora). The first two ones are bracketed format, the third one is an XML encoding. In all cases, the trees encoded do not look like the one in Figure  1 : the relations are annotated on the daughter nodes, on the satellite for mono-nuclear relations, or on all the nuclei for multi-nuclear relations. Moreover, in the rs3 format, the nuclearity of the segments is not directly annotated, it has to be retrieved using the type of the relation (indicated at the beginning of each file) and the previous principle.
Cross-lingual Discourse Parsing Our first experiments are strictly monolingual, and they are intended to give state-of-the-art performance in a fully supervised setting. We consider that we need at least 100 documents to build a monolingual model, since we already keep around 65 documents for test and development. We then evaluate multi-source transfer methods, considering one language as the target and the others as sources. More precisely, we will evaluate two settings: (1) training and optimizing only on the available source data; (  2 ) training on all available data, including target ones if any, and optimizing on the development set of the target language. Setting (1) provides performance when no data are available at all in the target language, while (2) aims at evaluating if one can expect improvements by simply combining all the available data.
We also distinguish between the "first" and the "last" EDU in the document, and use also a threshold on the ratio s =(position of the EDU divided by the total number of EDUs) to separate EDUs at the beginning (s < 0. 25), in the first middle (0. 25 ≤ s < 0. 5), in the second middle (0. 5 ≤ s < 0. 75) or in the end (s >= 0. 75). Position of the head We add a boolean feature indicating if the head of the sentence is in the current EDU or outside. Number/date/percent/money We also use 4 indicators of the presence of a date, a number, an amount of money and a percentage, features that have proven to be useful for discourse  (Pitler et al. , 2009). We build these features using simple regular expressions. All the results given are based on a gold segmentation of the documents. Each dataset is parsed using UDPipe, 18  thus tokenizing, splitting into sentences and annotating each document based on the Universal Dependency scheme  (Nivre et al.
, 2016). The word features for the non-English datasets are translated using available bilingual Wiktionaries 19  without disambiguation, the coverage of each dictionary is given in Table  2. We also look for a translation of the lemma (and of the stems for the languages for which a stemmer 20  was available) as a backup strategy. When no translation is found, we keep the original token. The word embeddings used were built on the EuroParl corpus  (Levy et al. , 2017). We keep only the 50 first dimensions of the vectors representing the words, our preliminary experiments suggesting no significant differences against keeping the whole 200 dimensions. Unknown words are represented by the average vector of all word vectors. For Basque, we had no access to these embeddings, we thus only report results using bilingual Wiktionaries.
Results Monolingual experiments Monolingual experiments are aimed at evaluating performance for languages having a large annotated corpus (at least 100 documents). Our results are summarized in Table  3. Our parser is competitive with state-ofthe-art systems for English (first line in Table  3 ), with even better performance for unlabeled structure (85. 04%) and structure labeled with nuclearity (72. 29%). These results show that using all the words in the units  (Ji and Eisenstein, 2014; Li et al. , 2014) , is not as useful as using more contextual information, that is taking more DUs into account (left and right children of the CDUs in the stack). However, the slight drop for Relation shows that 21 When using embeddings, the final vector is of size 350. 22 We use the evaluation script provided at https:// github.
Cross-lingual experiments When only relying on data from different languages ("Cross" in Table 3), we observe a large drop in performance compared to monolingual systems. The sourceonly discourse parsers still have fairly high performance for unlabeled structure (around 70% or higher), the scores being especially low for relation identification. This could indicate that our representation does not generalize well. But it also comes from differences among the corpora. For example, only the En-DT and the Pt-DT use the relation ATTRIBUTION. This leads to a large drop in performance associated with this relation, when one of these corpora is not in the training data, especially for the source-only system for the En-DT (from 93% in F 1 to 30%). On the other hand, on the En-DT, we observe improvement for other relations either largely represented in all the corpora (e.
5%) using only data in other languages. Word embeddings Using word embeddings ("+emb" in Table  3 ) for monolingual systems often leads to an important drop in performance, especially for Relation (from -1. 1 to -4. 2%). This demonstrates that these embeddings do not provide the large range of information needed for relation identification, a task inherently semantic. We believe however that the results are not too low to prevent for interesting applications. It is noteworthy that the English parser with embeddings is still better than the systems proposed in  (Hernault et al. , 2010; Joty et al. , 2013). For cross-lingual experiments, the bilingual dictionaries perform generally better than embeddings (except for Pt-DT and De-DT for sourceonly systems), demonstrating again that we need representations more tailored to the task to leverage all relevant lexical information.
Dictionary coverage for each dataset on the train set when available, on the dev set else. Data For the En-DT, we follow previous works in using the official test set of 38 documents. For the Es-DT, we report results on the test set A. 17 For all the other corpora, we randomly choose 38 documents to make a test set, and either use the remaining documents as development set (Nl-DT and Eu-DT), or split them into a development set of 25 documents, the remaining being used as training set (En-DT, Es-DT, Pt-DT and De-DT).
For each category of functional group (in bold), the most prominent biological function (in italic) has been annotated with the number of genes affected in that category, the total number of genes in that category, the statistical significance ( p-value) of the match and fold enrichment (FE). The most prominent GO categories among those upregulated in response to pico overexpression are related to protein biosynthesis, including initiation of translation, ribosomal function and protein maturation. There is also an enrichment in proteins localized to mitochondria. (b) Predicted interacting network for genes over-represented in response to pico overexpression, visualized using STRING. Potential associations are indicated by the links in the graph and colour coded by type: co-citation from the abstract of scientific literature (green), proteins related in curated databases (blue) and physical protein-protein for interaction databases ( pink).
rsob. royalsocietypublishing. org Open Biol. 7: 160306 significant induction of Actin5C, which has been proposed to be a homeostatic target of Mrtf/SRF in ovaries  [24] , nor did we see enrichment of GO categories corresponding to cytoskeletal genes among the overexpressed genes in DAVID or FLYMINE (electronic supplementary material, table  S2 ). To determine the relationship between the effects of mrtf and pico, we subjected our RNA-seq datasets to principal component analysis, which is not subject to thresholding and therefore has the ability to scrutinize all the available transcriptomics data in a non-biased fashion. We took this approach because we reasoned that Mrtf-SRF-responsive genes might show modest changes in transcript levels (similar to our reporter gene), but be biologically relevant when individual genes belong to a cohort of genes with related functions that show a consistent change in expression.
Correspondingly, we found differential expression of mRNA for core clock components timeless and clock, but not period, whose human orthologue is a target of Mrtf  [19]. This suggests that part of the divergent response might be explained by alterations in phasing of the circadian clock. Strikingly, the overexpression of ribosome protein and mitochondrial protein genes were highly significant common responses to pico and mrtf overexpression (figure  6c ). We wondered whether ribosome genes representing part of the common transcriptional signature (PC2) for mrtf and pico may be direct targets of Mrtf. To assess this we referred to a dataset of genes known to be bound by Mrtf in Drosophila adult ovaries based on ChIP-Seq  [24]. We examined the overlap between the top 10% of Mrtf-binding sites ranked according to their p-value score  [24] , and the top 10% of upregulated genes in PC2.
The site at 2598 showed significant enrichment following ChIP with anti-Flag antibody compared with controls (IgM ChIP in da. FLAG-SRF and FLAG ChIP in da-GAL4 extracts). A comparison with the Mrtf ChIP-Seq dataset  [24]  revealed an Mrtf-binding site corresponding to the SRF-binding site at 2598, suggesting both SRF and Mrtf are capable of associating at this site. Studies of other growth drivers, such as the miRNA bantam, have demonstrated that genes stimulating cell proliferation can simultaneously suppress apoptosis  [31]. Therefore, we wondered whether pico only drives net proliferation when apoptosis is simultaneously prevented in a deterin-dependent manner. To test this, we examined the effect of overexpressing pico in wing imaginal discs with or without deterin RNAi knockdown (deterin IR ) using MS1096-GAL4.
Discussion MRL proteins are key molecules that modulate the actin cytoskeleton in response to guidance cues to effect changes in cell morphology and migration. Additionally to this role, several lines of evidence suggest that MRL proteins also play an important role in cell growth in normal and pathological conditions  [15, 33]. This role in growth was highlighted in Drosophila where pico overexpression resulted in a coordinated increase in cell proliferation and growth  [14]. Here we have analysed global RNA expression to help delineate pathways linking the regulation of actin dynamics to tissue overgrowth. Genetic data previously implicated Mrtf/SRF in the ability of pico overexpression to drive wing overgrowth. Here, using a genetic reporter, we found that pico is capable of activating SRF-responsive gene expression in the Drosophila wing imaginal disc.
Reads were filtered for quality and mapped onto the Drosophila melanogaster reference genome version dm5. 39  [43]  using TOPHAT 2. 0  [43]. The number of reads mapping to each gene were calculated using HTSEQ-COUNT  [45] , and the count data were further analysed using EDGER  [46]. The data were normalized to account for differences in library size, and a generalized linear model was applied, using MS1906-GAL4 alone as the reference and contrasting this with MS1096. pico and MS1096. mrtf. p-values were obtained using t-tests, and adjusted using the Benjamini & Hochberg  [47]  multiple testing procedure to control the false discovery rate (FDR). An FDR cut-off of 0. 05 was applied to identify differentially expressed genes. RNA-Seq data have been deposited in the NCBI Sequence Read Archive, accession no.
Gene ontology analysis To analyse the enrichment of the genes belonging to specific biological processes, significantly down-or upregulated genes were analysed by Database for Annotation, Visualization and Integrated Discovery (DAVID)  [21]  against D. melanogaster database ( p-value, 0. 05; min. genes, 5) or using FLYMINE (www. flymine. org) [48]. The protein -protein interactions were obtained using STRING  [22]. Active prediction methods were experiments, databases and textmining and a medium confidence score (STRING global scores: 0. 4) was applied to identified the predicted interactome, which was based on experimental evidence, database association and co-citations.
Principal component analysis Principal component analysis was carried out using the open source statistical package R and the prcomp function. The loadings of the first 2 components were extracted, and the top 10% of the most positive and most negative loading genes were subject to DAVID gene-annotation enrichment and functional annotation cluster analysis  [21]. Each annotation cluster was summarized into a single term, taking the most significant term from each cluster, using an FDR cut-off of 10%. These terms were displayed in a bar plot using values of 210 Â log 10 of the Benjamini & Hochburg adjusted p-values. Literature mining was performed using FLYMINE  [48].
qRT-PCR qRT-PCR procedures were described previously  [16]. Briefly, 1 mg of total RNA samples were subjected to reverse-transcription using high capacity RNA-to-cDNA kit (Applied Biosystems/Invitrogen). Primer design was performed using PRIMER3 online software  [49]. cDNA were amplified in real time using the qPCR Master mix plus for power SYBR Green I assay (Invitrogen) and analysed with the StepOne-Plus real-time PCR system (Applied Biosystems). The level of gene expression in extracts from MS1096. UAS-pico, and MS1096. mrtf was compared with controls (MS1096-GAL4 alone) and expressed as a ratio. Primers used for qPCR are given in electronic supplementary material, text S1.
Image analysis For measurements of SRE-mCherry, image analysis was conducted using Bitplane IMARIS v. 8. 2. 0 (Oxford Instruments). The GFP channel was segmented into a single threedimensional volume (5 mm surface grain size) by absolute intensity using an automatically selected intensity threshold. Small unattached objects were removed using a volume filter. This segmented volume was used to mask the red channel rsob. royalsocietypublishing. org Open Biol. 7: 160306 into two new channels by zeroing all voxels inside or outside the volume. These new channels were used to segment the data into spots (estimated diameter of 5 mm, using background subtraction). Spots were subjected to an automatically thresholded intensity filter, which was visually inspected and adjusted if necessary. The mean intensity of each spot was recorded for each sample.
The GFP-positive fraction was calculated as the number of GFP-positive spots divided by the total number of spots. Intensity bias was calculated as the mean intensity of GFP-positive spots divided by the mean intensity of GFP-negative spots. Using the data above, the individual mean spot intensities were scaled to 8-bit values and histograms were produced using MATLAB 2015a (Mathworks). Data were binned into 13 bins spread evenly across the 8-bit range for all conditions. The histograms were then normalized by dividing all frequency values by the maximum frequency value within that dataset. Data were plotted in GRAPHPAD, along with nonlinear regression curve assuming a Gaussian distribution. For quantitation of cleaved Caspase 3 staining, data were segmented into spots as described above using IMARIS.
Acknowledgements. We thank the Bloomington Stock Center and Pernille Rorth for Drosophila strains, the  Drosophila Genome Resource Centre  and  Sarah Bray  for plasmids,  Genetic Services Inc. for generation of transgenic lines,  Wei Du  and  Christian Lehner  for antibodies,  Pernille Rorth  for supplementary ChIP-Seq data,  Marco Marcello ,  Centre for Cell Imaging University of Liverpool  for assistance with image acquisition,  Roy Chaudhuri ,  Xuan Liu  and the  Centre for Genomics Research, University of Liverpool  for RNA-Seq and bioinformatic analysis,  Mirel Lucaci  and  Simon Fredeval  for technical assistance.
Funding. This work was funded by  Cancer Research UK  (grant  C20691/A11834 ) and  Medical Research Council UK  (grant  MR/ K015931/1 ). N. A. was supported by the  Saudi Arabian Ministry of Higher Education ; L. D. was supported by an  MRC   Capacity Building studentship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Advances in high-throughput DNA sequencing combined with databases that allow the sharing of clinical and genotypic information among clinicians and researchers, such as those in the Matchmaker Exchange hub, have changed the landscape of research collaborations leading to accelerated disease variant discovery and validation for neurocognitive disorders during recent years. 1, 2 This has led to an explosion in the diagnostic rate for neurocognitive disorders,  1  with increasing appreciation of the complexity of clinical presentations of variants even in the same gene. In a multicenter study, we discovered a recurrent proteintruncating variant in ZSWIM6 (MIM: 615951) in a cohort of affected individuals with overlapping neurocognitive phenotypes. A recurrent de novo nonsynonymous ZSWIM6 variant (GenBank: NM_020928.
1 [ZSWIM6]; c. 3487C>T [p. Arg1163Trp]) had previously been reported to cause the rare Mendelian disorder acromelic frontonasal dysostosis (AFND)  3, 4  characterized by severe frontonasal dysplasia, tibial hemimelia, pre-axial polydactyly, brain malformations, and severe neurocognitive and motor delay. 3, 4 The p. Arg1163Trp variant was postulated to perturb the function of the highly conserved Sin3-like domain at the C terminus of the protein, with three-dimensional modeling suggesting disruption of an interaction surface. 4 A gainof-function mechanism was proposed, based on the observation that affected individuals with chromosomal deletions spanning ZSWIM6 5 lack the craniofacial and limb abnormalities seen in AFND. 4  By sharing variants identified from exome or genome sequencing with Matchmaker Exchange  6  or via contact with individual diagnostic laboratories, we identified seven unrelated individuals (four female, three male) with a recurrent single-nucleotide variant in ZSWIM6 that introduced a premature termination codon (PTC) in exon 13 (chr5[GRCh37]: g60837744C>T; GenBank: NM_020928.
] [10]  Whole-genome sequencing (WGS) was utilized for individuals 1 and 6. DNA from individual 1 was sequenced on the Illumina HiSeqX platform at the Kinghorn Centre for Clinical Genomics, Sydney, obtaining $120 Gb data per sample, equivalent to >303 average coverage. Variant alignment, single nucleotide and short indel variant identification, annotation, and quality assessment was performed as previously described. 8 Read alignment bam files were used as input to call structural variation including copy-number variation with ClinSV (unpublished data). Variants were annotated using the ENSEMBL gene set v75  11  and previously reported structural variants from the Database of Genomic Variants. 12 Candidate variant calls and the corresponding read-alignments were inspected using the Integrative Genomics Viewer.