dataesr/scientific-paragraphs-categorization huggingface dataset:
truncatula with the hemibiotrophic leaf pathogen Colletotrichum trifolii or with the biotrophic soil pathogen Phytophtora medicaginis  [31]. Altogether, our data and the study from  [31]  are in agreement with the MtGEA dataset and broaden the conclusion that NCRs are not involved in pathogen responses, whatever the trophic (bio-, hemibio-or necrotrophic) interaction, the host or non-host status and the output of the interaction (disease or resistance). Finally, as herbivory and more generally wounding may induce plant defenses around the wounded zone, we also tested the effect of mechanical wounding on NCR expression but again no NCR expression could be detected in the wounded leaflet (Additional file 2: Figure  S12 ).
For nodule, seed and root the mean value of different developmental stages or experiments was used (for "nodule": 4, 10, 14 and 28 dpi stages; for "seed": 10, 12, 16, 20, 24 and 36 dap; for "root" the 0 dpi control for nodulation and an independent experiment); thus in total 19 experiments were used. The Shannon entropy was calculated as described (see Methods section)  [47, 48]  for each of the 50,900 probe-sets using these 10 tissue datasets. The 9000 probe-sets with the lowest entropy (and therefore the highest tissue specificity) were selected for further analysis (Additional file 3: Table  S2 ). A hierarchical cluster analysis of these 9000 genes was calculated (Figure  5A ). Clusters of tissue specific genes can be distinguished for root, seed, pod, flower, aerial tissues (l/p/s/b), root tissues (r/n/m), nodule and mycorrhiza.
The seed, flower, mycorrhiza and especially the nodule clusters are enriched in genes with low entropy value E g (Figure  5A ). The strong enrichment of nodule-specific genes among the low entropy genes becomes more obvious when re-ordering the dataset according the increasing entropy levels (Figure  5B ). This analysis shows that in M. truncatula the genes with the lowest entropy and thus the highest tissue specific expression are mostly nodule-specific genes and to a lesser extent, seed-and flower-specific genes. Strikingly, among the nodule-specific genes, the NCRs are the most represented ones (Figure  5B ). Although the NCR family is by far the most represented among the nodule-specific low entropy genes, many other known nodule specific genes have very low entropy (Additional file 3: Table  S2 ).
However, the MtGEA dataset from auxin treated seedlings do not show any NCR induction, pointing out a more complex regulatory mechanism controlling NCR expression. The very tight regulation of the NCR genes that was revealed here might indicate that besides cisand transacting factors, regulation at the level of chromatin might also be involved in the activation of the NCR genes. Moreover, endoreduplication seems to be a prerequisite for their activation  [19]  and might thus, by a presently unknown mechanism, be implicated in the activation of this gene family. In that respect, it is interesting to note that the giant feeding cells induced by the nematode M. incognita are highly polyploid cells and possibly express faintly a few NCR genes. Nevertheless, this observation could be an experimental artefact and will require further experimental confirmation.
Analysis of MtGEA data The MtGEA transcriptome compendium was downloaded from the website of the Samuel Roberts Noble foundation (http://mtgea. noble. org/v3/). The data from  [22, 23]  were obtained from the NCBI Gene Expression Omnibus (accession n°GSE53406 and GSE43354 respectively). All the data were imported in Excel (Additional file 1: Table  S1 ) for extracting the expression profiles of the 334 NCR probe-sets and for further treatments. The NCR probesets on the Affymetrix Medicago GeneChip, which was used for the MtGEA transcriptome compendium, were obtained by BLASTn searches on the MtGEA website (Additional file 1: Table  S1 ). Each individual NCR nucleotide sequence resulted in the identification of multiple probe-sets due to the homology between NCR gene sequences. In total 334 different probe-sets were retrieved.
This collection represent likely nearly all NCR probe-sets present on the Affymetrix Medicago GeneChip and the remaining genes identified in Young et al. [17]  and Zhou et al. [15]  are missing from these arrays because they were not yet annotated at the time of array design. Cluster analysis of the complete MtGEA dataset was performed using the MeV software package (http:// sourceforge. net/projects/mev-tm4/). Briefly, the Excel datasheet extracted from MtGEA was analyzed using the Euclidean distance application with average linkage settings. Heat maps were generated with MeV and histograms and graphs with Excel.
Entropy calculations Calculations were performed on the MtGEA dataset in Excel. For the normalization of expression levels in N tissues, the relative expression P t/g of a gene g in a tissue t was calculated as P t/g = W t/g /Σ 1≤t≤N W t/g where W t/g is the expression level of the gene g in the tissue t. The Shannon entropy E g of gene g is calculated as E g = Σ 1≤t≤N -P t/g log 2 (P t/g ). E g ranges from zero for genes expressed in a single tissue to log 2 (N) for genes expressed uniformly in all tissues considered. Heat maps of entropy values were generated by the MeV software package.
It should be noted that each of these hand-dissected samples is enriched for the indicated zone but can contain cell layers form the adjacent as well. Total RNA extraction and purification were conducted as described  [20]. For hybridization onto the Affymetrix M. truncatula Genechip Array probes were synthesized and labelled from 500 ng RNA using the Gene Chip 3′ IVT express kit following manufacturer's guidelines (Affymetrix). Global normalization of expression was carried out using the Robust Multiarray Average Express software  [64].
1 suspensions of S. meliloti strain 1021 or Sinorhizobium arboris strain B554 (synonymous strain names LMG 14919 and HAMBI 1552)  [69]  which is an excellent symbiont of M. truncatula R108 forming numerous large, nitrogen fixing nodules. Samples were collected at indicated time points and embedded in 6% agarose. Tissue sections of 70 μm were prepared with a Leica VT1200S vibratome. GUS staining was done as described  [70]  and was allowed to proceed for 1 h (Additional file 2: Figure  S6 ). Overnight staining did not alter the expression patterns (data not shown). The pattern of expression of the NCR genes in nodules induced by both Sinorhizobium strains were very similar. For all pathogen assays, plants were cultivated on perlite/ sand (3/1 vol/vol) substrate and watered with a commercial nutrient solution.
Antibodies and immunolocalization The mature region of the NCR122 peptide was amplified from cDNA and cloned into the expression vector pBAD-gIII/A (Invitrogen). Recombinant proteins were purified according to the manufacturer's instructions and used for immunization of rabbits by a commercial service (Agrobio). Immunolocalisations were done exactly as described before  [8]. For the SYTO13 nucleic acid staining, nodules sections were incubated for 5 minutes with 1 μM SYTO13 in H 2 O. Immuno-or SYTO13-stained sections were mounted in deionised water for confocal imaging. Fluorescence images were acquired at 1024×1024 pixels resolution with the confocal laser scanning microscope TCS SP2 from Leica, using 10X water-immersion and 63X oilimmersion objectives and Leica software. Images were processed with Adobe Photoshop for adjustment of contrast and brightness.
Additional files Additional file 1: Table  S1. NCR probe-sets and expression data. The NCR probe-set annotations are from the Affymetrix Medicago GeneChip. The expression data were extracted from MtGEA, NCBI GEO, and from the spatially-resolved nodule dataset. Additional file 3: Table  S2. Entropy of gene expression. The 9,000 probe-sets with the lowest entropy.
Soil-borne vascular wilt diseases caused by Verticillium spp. are among the most destructive diseases worldwide in a wide range of plant species. The most effective means of controlling Verticillium wilt diseases is the use of genetic resistance. We have previously reported the identification of four activation-tagged Arabidopsis mutants which showed enhanced resistance to Verticillium wilt. Among these, one mutant also showed enhanced resistance to Ralstonia solanacearum, a bacterial vascular wilt pathogen. Cloning of the activation tag revealed an insertion upstream of gene At3g13437, which we designated as EWR1 (for Enhancer of vascular Wilt Resistance 1) that encodes a putatively secreted protein of unknown function. The search for homologs of Arabidopsis EWR1 (AtEWR1) in public databases only identified homologs within the Brassicaceae family.
EWR1 encodes a protein of unknown function The full-length genomic DNA sequence of EWR1 consists of 599 bp containing two exons of 97 and 116 bp separated by an intron of 87 bp (Figure  6A ). The ORF encodes a protein of 70 amino acids with a predicted molecular mass of 7. 93 kDa which is annotated as unknown. With SignalP 3. 0 (http://www. cbs. dtu. dk/ services/SignalP/)  [25]  it is strongly predicted that EWR1 contains a signal peptide of 21 amino acids. Searching for recognizable protein domains or signatures of conserved motifs in the EWR1 protein sequence in publicly available databases did not result in any significant hits.
A B. oleracea EWR1 homolog provides Verticillium wilt resistance in Arabidopsis A tblastx search using the nucleotide sequence of EWR1 in the NCBI database (http://blast. ncbi. nlm. nih. gov/Blast. cgi) identified only three significant hits, all within the Brassicaceae family. The hits were with Arabidopsis lyrata (E-value = 1e-15), Sisymbrium irio clone SIR-40E09 (E-value = 4e-11) and Brassica rapa subsp. pekinensis clone KBrH040N18 (E-value = 9e-08) (Figure  6B ). No EWR1 homologs were identified in other plant species, suggesting that EWR1 is a Brassicaceae-specific gene. Based on the sequence conservation, we designed primers to amplify the EWR1 homologue from yet another Brassicaceous species, Brassica oleracea, using genomic DNA. Indeed a homolog, designated BoEWR1, was amplified which reinforced the suggestion that EWR1 is a Brassicaceae-specific gene.
We next aimed to test whether BoEWR1 expression in Arabidopsis also provides Verticillium wilt resistance. To this end, BoEWR1 was amplified from B. oleracea cDNA and constitutively expressed in the Arabidopsis Col-0 and WS ecotypes. As expected, BoEWR1 expressing plants displayed similar leaf morphology as AtEWR1 expressing plants (Figure  7A ). Subsequently, BoEWR1 expressing plants were challenged with V. dahliae, revealing enhanced resistance to Verticillium wilt when compared with wild-type plants (Figure  7A, B ). Real-time PCR analysis confirmed reduced Verticillium colonization on BoEWR1 expressing plants when compared with wild-type plants (Figure  7C ). These data show that the EWR1 homologs of Arabidopsis thaliana (AtEWR1) and Brassica oleracea (BoEWR1) are functional homologs with respect to their role in Verticillium wilt resistance.
These findings suggest that the induction of developmental aberrancies and Verticillium wilt resistance can be uncoupled. EWR1 encodes a mature protein of 49 amino acids with unknown function. Homologs are only found in Brassicaceae species, showing high sequence conservation at the C-termini and more diversity at the N-termini. The C-termini of the B. rapa, B. oleracea and S. irio homologs, but not of the A. lyrata homolog, contain two adjacent cysteine residues. Although cysteine residues are often implicated in disulphide bond formation to enhance protein stability, the adjacent localization of these residues in EWR1 makes intramolecular disulphide bond formation unlikely. However, possibly the cysteine residues might be involved in EWR1 homodimerization. The search for recognizable protein domains in EWR1 did not result in any significant hits in publicly available databases.
Determination of the activation-tag insertion site The activation-tag insertion site in mutant A2 was determined using thermal asymmetric interlaced PCR (TAIL-PCR)  [46]. The PCR was performed with a combination of nested primers  [47]  and 10-mer random primers  [48]. The secondary and tertiary TAIL-PCRs were separated on 1. 2% agarose gel, stained with ethidium bromide, and visualized using the ChemiDoc XRS system (Bio-Rad). Specific product, judged based on the size differences generated by the nested primers, was excised, cleaned using the QIAquick Gel Extraction Kit (QIAGEN), cloned into the pGEM-T Easy Vector (Invitrogen), and sequenced. Blastn search of the TAIR database using the PCR sequences was performed to identify the genomic insertion site. Based on the putative insertion site, the primer pair MPR15F and MPR15R were designed and used to amplify the flanking genomic region.
Cloning of EWR1 homologs Primer pair EVR1H-BrF0 and EVR1H-BrR1 was used to amplify BoEWR1 from genomic DNA (gDNA) of Brassica oleracea (Brussels sprout). The PCR product was excised from the gel, cleaned (GE Healthcare) and cloned into the pGMET-easy vector (Promega) and sequenced. Based on the sequence alignment of the PCR sequence and the B. rapa sequence in the database, primer EVR1H-BrR3 was designed and used in combination with EVR1H-BrF0 to amplify the predicted full length CDS of BoEWR1 from B. oleracea cDNA. As a control, the same primer combination was used to amplify BoEWR1 from gDNA. The PCR fragments were sequenced to confirm the full length CDS. To generate an BoEWR1 over-expression construct, the full length CDS of BoEWR1 was amplified from cDNA using primer pair EVR1H-BaF1 and EVR1H-AsR1 containing BamHI and AscI custom restriction sites, respectively, and cloned into BamHI and AscI pre-digested binary vector pB7K40  [23].
Pathogen quantification in planta Real-time PCR was used for quantification of pathogen colonization in planta using an ABI7300 PCR machine (Applied Biosystems) in combination with the qPCR Core kit for SYBR Green I (Eurogentec, Maastricht, The Netherlands) and analyzed using the 7300 System SDS software (Applied Biosystems). Unless described otherwise, the primer pair AtRub-F4 and AtRub-R4 targeting the gene encoding the large subunit of RuBisCo was used as endogenous control. Verticillium and R. solanacearum colonization was assessed as previously described  [13] ,  [23].
Introduction Profound congenital deafness affects about 1 out of 1000 newborns  [1] , and is mainly of genetic origin. Non-syndromic (isolated) deafness accounts for approximately 70% of inherited cases. To date, around 70 genes and more than 1000 mutations causing non-syndromic deafness have been reported (http:// deafnessvariationdatabase. org). More than 45 genes and 69 loci are associated with autosomal recessive non-syndromic deafness (DFNB). Despite the broad genetic heterogeneity of DFNB, lossof-function mutations in a single gene, GJB2 (encoding connexin-26) account for more than 30% of the cases in most populations around the Mediterranean sea  [2]. After the exclusion of GJB2 in DFNB patients, finding the gene implicated is difficult due to the high degree of genetic heterogeneity  [3].
14 with default parameters  [9]. The exome design covers 51 Mb of the human genome corresponding to the exons and flanking intronic regions of 20 766 genes (220 000 exons) and also 700 miRNAs in the human reference sequence UCSC (hg19/GRCh37, February 2009 release)  [7]. Bioinformatics analysis of sequencing data was based on the Illumina pipeline (CASAVA 1. 8). CASAVA aligns reads to the human reference genome (hg19) with the alignment algorithm ELANDv2 (it performs multispeed and gapped alignments), calls SNPs on the basis of allele calls and read depth, and detects variants (SNPs and indels). Only positions included in the bait coordinates were conserved. Genetic variation was annotated with the IntegraGen in-house pipeline, consisting of gene annotation (using RefSeq), detection of known polymorphisms (using dbSNP132 and the 1000 Genomes Project database) and characterization of mutations as intronic or exonic, and silent, nonsense, missense and frame-shifting  [9].
Results Mutations in GJB2, the gene most frequently involved in autosomal recessive deafness in Tunisia  [10] [11] [12] , and mutations in other DFNB genes that had previously been reported in Tunisian deaf patients (Table  S1 ) were first excluded by PCR and Sanger sequencing of these genes in the patients. For the WES data analysis, based on familial history and pedigree we hypothesized an autosomal recessive mode of disease transmission and the presence of the causative mutations in the homozygous state in the patients. To identify pathogenic variants, we filtered out polymorphisms using the Single Nucleotide Polymorphism Database dbSNP132. We excluded all the variants reported in 1000 genomes, Hapmap, and Exome variant server databases. In the second step, we focused on variants which are present in the coding exons and flanking splice sites.
Discussion We have identified previously unreported biallelic mutations in three different DFNB genes (MYO15A, LRTOMT, TMC1) in patients affected by congenital profound deafness, who belong to four unrelated Tunisian families, using a WES strategy. In the DF7 patients, a missense mutation, c. 5417T. C (p. L1806P), was identified in exon 22 of MYO15A (NM_016239). This mutation is predicted to be deleterious according to SIFT (http://sift. jcvi. org/) and Mutation Taster (http://www. mutationtaster. org/). In family DF137, a different mutation, c. 7395+3G. A, was present in the same gene. This mutation is predicted to abolish the splice donor site of intron 37 and to create a cryptic splice donor site 3 bp upstream from the original site, according to the Alamut 2. 3 software (http://www. interactivebiosoftware.
3 software. LRTOMT is responsible for the Table  1. Sequences of the primers used to validate the mutations by Sanger sequencing. Table  3. Biallelic mutations identified in DFNB genes using a whole exome sequencing strategy. DFNB63 deafness form. It produces five different alternatively spliced transcripts, which encode two different leucine rich transmembrane and O-methyltransferase domain containing proteins, LRTOMT1 and LRTOMT2, both expressed in the inner ear sensory cells  [18]. Mice carrying a mutation of the orthologous gene (COMT2) suffer from vestibular dysfunction, profound deafness and progressive degeneration of the organ of Corti  [19]. Two mutations in LRTOMT had previously been reported in Tunisian patients, c. 242G. A (p. R81Q) and c. 313T. C (p. W105R). Two other mutations, c.
This work was supported by the  Tunisian Ministry of Public Health , the  Ministry of Higher Education and Scientific Research  ( LR11IPT05 ) and by the  E. C. Grant agreement Nu  295097  for  FP7  project  GM-NCD-Inco  and  BNP Paribas foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
A steepest gradient descent was used to compute the minimization for u in  [29] , with the bisection method to search a suitable time step at each iteration (it usually requires about 30 sub-iterations to find the time step). We used the same algorithm, but significantly accelerated the code to avoid unnecessarily repeated computations. We observed that with this acceleration, the CPU time for one iteration was approximately the same for the LM-Uzawa and the proposed algorithms, but as we shall see later the convergence of the LM-Uzawa algorithm remains significantly slower. The choice of ρ 0 (the initial time step for updating λ D , see  [29]  for details) also has a strong impact on the performance of the LM-Uzawa algorithm. Large ρ 0 results in a good control of the constraints, but may also cause bad convergence behavior.
We set λ = 5 for the ROF model and the proposed model. Fig. 8  displays the restored images. 2 The MATLAB code is available on http://www. cs. cityu. edu. hk/∼xbresson/ucla/code. html Then, we choose a uniform 7 × 7 blur to corrupt the original image, and add as before a Gaussian white noise with mean zero and standard deviation 2 to it. Again, the parameter λ is set to 5. The restored images are shown in Fig. 9. Finally, we apply the proposed algorithm to a motion-blurred image. The kernel is generated by the MATLAB function "fspecial" with approximately 30 pixels and an angle of 45 •. Then the blurred image is corrupted with an additive white Gaussian noise with mean zero and standard deviation 1. With the parameter λ = 20, the restored images are shown in Fig. 10. Fig. 11  shows the resulting RDS of the three experiments mentioned above.
The divergence of the dual variable is defined by (div w p) (x) = Ω (p(x, y)p(y, x)) w(x, y)dy, and we have ∇ w u, p =u, div w p. We can easily replace TV regularization by the NLTV regularization in the proposed algorithm. In the following, we give some experimental results of the proposed algorithm and the NLTV restoration model which can be solved by Algorithm 1. To compute the weights (the values of the all pixels of the degraded image are scaled in the range [0, 1] when computing the weights), the MATLAB code provided by Bresson 3  was used. We set the patch size to 9 × 9, the searching window size to 11 × 11 and the number of neighbors to 10. The filtering parameter was set to h = 0. 45 for image denoising, h = 0. 01 for restoration from Gaussian blur and uniform blur, and h = 0. 005 for motion deblurring.
, 2012)  and to histone gene transcription in chicken  (Hsin et al. , 2011) , and Tyr1P that in yeast is found at gene body locations, consistent with a role in transcription elongation  (Mayer et al. , 2012). This latter modification remains however so far uncharacterized in mammalian cells and we aimed at deciphering its function in human cells using biochemical and genome-wide approaches. To assess its relation to transcription genome-wide, we next performed Tyr1P ChIP-seq, using 3D12 mAb, and compared it to Pol II and the other phospho-isoforms. We isolated significantly associated regions based on the signal distribution of the background-subtracted data (Figure  2 figure supplement 1B) and found that Pol II and all isoforms, including Tyr1P, correlated with transcription levels of genes (Figure  2 -figure supplement 2).
Quality control and filtering In brief, all samples were sequenced on an Illumina Genome Analyzer (GAIIx for ChIP-seq and RNAseq, or HIseq2000 for MNase-seq and H3K27ac). Quality assessment and filtering of ChIP-seq and MNase-seq sequences were performed using either the Integrated Eland software or FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_toolkit/index. html) to pre-process FastQ files. Quality score and nucleotide composition at each position of the sequenced tags were assessed by box and bar plotting using FastX-Toolkit standard functions. Quality controls (QC) and filtering of RNA samples were performed using fastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/), FASTX-toolkit, and Cutadapt (http://code. google. com/p/cutadapt/). Adapters were removed (Cutadapt) and a QC report was generated (fastQC).
Tyr1P peak sorting and corresponding clusters For Tyr1P sorting (Figure  3 , Figure  3 -figure supplement 1), an in-house script (Source code 1) was used as described previously in  Fenouil et al. (2012). Briefly, taking the genes selected as having significant level of Pol II, genes were ordered according to the relative increasing distance to the TSS of the maximal peak of Tyr1P. Other clusters show the corresponding signal of Pol II, Ser5P, Ser7P, nucleosomes, and short ssRNAs on the same genes. Heatmaps were formatted in terms of color and contrast according to sample read depth using Java TreeView software  (Saldanha, 2004).