dataesr/scientific-paragraphs-categorization huggingface dataset:
P values were subsequently corrected for multiple testing using the q-value method  [37]  from the qvalue package in R. To summarize results afterwards, for all sites (that are heterozygous in at least one individual) we calculated how many individuals are heterozygous and how many show allelic imbalance, both in the total set (n = 205) as well as in the donors (n = 108) and DCM (n = 97) samples separately. Allelic imbalance differences between DCM and donors were calculated in two ways. Difference in number of imbalanced individuals was calculated using a Fisher's exact test (FET; 'FET P value' abbreviated as 'FET P'). The difference in imbalanced allele was determined using a test of proportions. In assessing differentially imbalanced sites between DCM cases and non-diseased donors we applied LD pruning on the set of imbalanced variants using results from SNAP (1000 Genomes; R2 > 0.
8) to keep only independent variants. Functional enrichment of imbalanced sites was performed using the NEXUS variant annotation tool  [80]. Additionally, overlap with known truncating variants and known imprinted genes (source http://www. geneimprint. com/site/genes-by-species. Homo+sapiens; accessed 14-01-2016) was determined. To assess co-occurrence of miRNA binding sites, only conserved predicted sites from TargetScan 7. 0 were used (accessed 30-03-2016). Overrepresentation of eQTL and differential splicing identified in the present study was determined using odds ratios with confidence intervals. Functional enrichment of associated genes was performed using the topGO package in R, using the parent-child algorithm  [81]  with a minimum node size of 5.
Acknowledgements We thank  Sebastiaan van Heesch  and  Jan Krumsiek  for insightful discussion and proofreading of the manuscript. We thank the personnel at the  Royal Brompton and Harefield Biomedical Research Unit Biobank , the  Sydney Heart Bank , and the personnel involved in biobanking the cardiac tissue at Szeged,  Miami , and  Vanderbilt  for the careful collection of cardiac samples. Part of this work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. Funding This work was supported by funding to MH by the  Federal Ministry of Education and Research (BMBF, Germany)  in the project eMed:symAtrial ( 01ZX1408D ), funding by the  Federal Ministry of Education and Research (BMBF, Germany)  in the project  NGFNPlus  ( 01GS084 ) to MS. CRB and EML were supported by the  Netherlands Heart Foundation  PREDICT project ( CVON 2012-10 ).
Strain identification Matrix-assisted desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis of strains SN19 T was performed on a MicroFlex mass spectrometer (Bruker Daltonics, Leipzig, Germany) as previously described  [8]. The acquired spectrum was then loaded into the MALDI Biotyper Software (Bruker) and analyzed as previously described  [8]  by using the standard pattern-matching algorithm, which compared the spectrum acquired with that present in the library (Bruker database and ours, constantly updated), including 7463 species. Score values of 1. 7 but <2 indicated identification beyond the genus level, and score values of 2. 0 indicated identification at the species level. Scores of <1. 7 were interpreted as not relevant. In this case, we performed the 16S rRNA gene sequencing.
For DNA extraction, we used the EZ1 DNA Tissue Kit using Biorobot EZ1 Advanced XL (Qiagen, Courtaboeuf, France). The amplification of the 16s rRNA gene was carried out using PCR technology and universal primers FD1 and RP2  [8]  (Eurogentec, Angers, France). Then we realized the purification, sequencing and assembly of the amplified products as previously described  [9]. Sequences of 16S rRNA genes were confronted with those which are available in GenBank by BLASTn (Basic Local Alignment Search Tool) (http://www. ncbi. nlm. nih. gov/GenBank/). When the percentage of identity was <98. 7%, the studied strain was considered to be a new species  [10].
Phylogenetic analysis A custom Python script was used to automatically retrieve all species from the same family of the new species and download 16S sequences from National Center for Biotechnology Information (NCBI) by parsing NCBI eUtils results and the NCBI taxonomy page. It only keeps sequences from type strains. In case of multiple sequences for one type strain, it selects the sequence obtaining the best identity rate from the BLASTn alignment with our sequence. The script then separates 16S sequences in two groups: one containing the sequences of strains from the same genus (group a), and one containing the others (group b). It only keeps the 15 closest strains from group a and the closest one from group b. If it is impossible to get 15 sequences from group a, the script selects more sequences from group b to get at least nine strains from both groups.
FAME were prepared as described by Sasser  [11]. GC/MS analyses were carried out as previously described  [12]. Briefly, fatty acid methyl esters were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500-SQ 8 S; Perkin Elmer, Courtaboeuf, France). A spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (National Institute of Standards and Technology, Gaithersburg, USA) and the FAMEs mass spectral database (Wiley, Chichester, UK). Microscopy. Mobility and Gram staining of strain SN19 T were determined using an optical microscope (Leica, Wetzlar, Germany) with a 40× and 100× oil-immersion objective lens, respectively. In order to observe the cells' morphology, they were fixed with 2. 5% glutaraldehyde in 0.
Genome annotation and comparison Open reading frames (ORFs) were predicted using Prodigal  [13]  with default parameters, but the predicted ORFs were excluded if they spanned a sequencing gap region (contain N). The predicted bacterial protein sequences were searched against the Clusters of Orthologous Groups (COGs) using BLASTP (Basic Local Alignment Search Tool; E value 1e-03, coverage 0. 7 and identity percentage 30%). If no hit was found, it was searched against the NR database using BLASTP with E value of 1e-03 coverage 0. 7 and an identity percentage of 30%, and if the sequence length was smaller than 80 aa, we used an E value of 1e-05. The tRNAScanSE tool  [14]  was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer  [15]. Lipoprotein signal peptides and the number of transmembrane helices were predicted using Phobius  [16].
ORFans were identified if all the BLASTP performed did not give positive results (E value smaller than 1e-03 for ORFs with a sequence size larger than 80 aa or an E value smaller than 1e-05 for ORFs with sequence length smaller than 80 aa). Such parameter thresholds have already been used in previous studies to define ORFans. Genomes were automatically retrieved from the 16S RNA tree using Xegen software (PhyloPattern)  [17]. For each selected genome, the complete genome sequence, proteome genome sequence and ORFeome genome sequence were retrieved from the NCBI's FTP site. All proteomes were analyzed with proteinOrtho  [18]. Then for each couple of genomes a similarity score was computed. This score is the mean value of nucleotide similarity between all couple of orthologues between the two genomes studied (average genomic identity of orthologous gene sequences, AGIOS)  [19].
An annotation of the entire proteome was performed to define the distribution of functional classes of predicted genes according to the clusters of orthologous groups of proteins (using the same method as for the genome annotation). To evaluate the genomic similarity among studied Prevotella strains, we determined two parameters, digital DNA-DNA hybridization (DDH), which exhibits a high correlation with DDH  [20, 21]  and AGIOS  [19] , which was designed to be independent from DDH. Annotation and comparison processes were performed in the Multi-Agent software system DAGOBAH  [22] , which includes Figenix  [23]  libraries that provided pipeline analysis. The genome of strain SN19 T was compared with the one of closest species such as Prevotella oralis (AEPE00000000), Prevotella micans NMNI (BAKH00000000), Prevotella stercorea (AFZZ00000000), Prevotella timonensis (CBQQ000000000), Prevotella shahii (BAIZ00000000), Prevotella loescheii (ARJO00000000), Prevotella saccharolytica (BAKN00000000), Prevotella oulorum (ADGI00000000) and Prevotella marshii (AEEI00000000).
Classification and features Strain identification by MALDI-TOF MS and 16S rRNA sequencing. Strain SN19 T was first isolated in November 2015 from the stool that was used to diagnose a C. difficile infection, stored at -80°C and cultured on Columbia agar enriched with 5% sheep's blood at 37°C during 5 days under anaerobic conditions. The MALDI-TOF spectrum (Fig. 1 ) did not match either Bruker's database or our database, and was consequently added (http://www. mediterranee-infection. com/article. php? laref=256&titre=urms-database). Then the 16S rRNA gene sequencing demonstrated that the type strain Prevotella oralis ATCC 33269 (GenBank accession no. NR_042841. 1) is the phylogenetically closest to strain SN19 T and showed a 97. 70% sequence identity with it (Fig. 2 ). This value was lower than the 98.
7% 16S rRNA gene sequence threshold advised by Stackebrandt and Ebers  [10]  to describe a new species without carrying out DNA-DNA hybridization. Therefore, we propose strain SN19 T as the type strain of a new species within the Prevotella genus, for which we proposed the name Prevotella phocaeensis sp. nov. (Table  1 ). The 16S rRNA gene sequence was deposited in European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) under accession number LN998069. A gel view allowed us to visualize spectral differences with other Prevotella species (Fig. 3 ). Phenotypic description. Prevotella phocaeensis strain SN19 T grew at 37°C on Colombia agar (with 5% sheep's blood) after 5 days in blood culture bottles enriched with rumen fluid (5%) and blood (5%) in anaerobic conditions.
NMNI order to decipher the role of Prevotella phocaeensis in health and diseases and particularly plant catabolism, glucose regulation and its interaction with Clostridium difficile in the human gut. Some cases of Clostridium difficile infection were treated by transplantation of faecal microbiota  [36]. Maybe the presence of P. phocaeensis, a probable member of the human mature anaerobic gut microbiota  [6] , explains the good outcome of the patient with Clostridium difficile infection. Therefore, considering phenotypic differences with the closest species, the proteomic profile obtained with MALDI-TOF MS of strain SN19 T , its phylogenetic analysis of the 16S       The G+C content of the genome is 44. 6%. The accession numbers of the sequences of 16S rRNA and genome which were deposited in EMBL-EBI are LN998069 and FIZG00000000, respectively.
Classification and general features of Prevotella NMNI remainder of the genes were annotated as hypothetical proteins (971 genes, 39. 96%). Table4summarizes the properties and the statistics of the genome and Table5the distribution of predicted genes of strain SN19 T according to COGs categories. The genome sequence was deposited in EMBL-EBI under accession number FIZG00000000. Genome comparison with other Prevotella species. The draft genome sequence of strain SN19 T is smaller than that of Prevotella saccharolytica, Prevotella loescheii, Prevotella timonensis, Prevotella stercorea and Prevotella shahii (2922, 2976, 3509, 3156, 3097 and 3490 MB respectively), but larger than that of Prevotella oralis, Prevotella oulorum, Prevotella marshii and Prevotella micans (2840, 2806, 2559 and 2427 MB respectively).
00 Gene associated with bacteriocin genes 7 0. 29 Gene associated with mobilome 670 27. 57 Gene associated with toxin/antitoxin 30 1. 56 Protein associated with ORFan 12 0. 49 Genes associated with PKS or NRPS 7 0. 29 COGs, Clusters of Orthologous Groups database; NRPS, nonribosomal peptide synthase, PKS, polyketide synthase. a Total is based on either size of genome in base pairs or total number of proteincoding genes in annotated genome. rRNA.
All the above research works were conducted using different methods and techniques beside data difference and integration as a cascading component. Moreover, dataset and tools used in the above research are not accessible which makes difficult to evaluate the advancement of research in speech technology for local languages. However, there is no attempt to integrate ASR, SMT and TTS to come up with speech translation system for Amharic language. Thus, the main aim of this study is to investigate the possibility to design Amharic-English speech translation system that controls recognition errors propagating through cascading components.
A parallel Amharic-English corpus has been prepared by translating the English BTEC data using a bilingual speaker. This data is used for the development of speech translation cascading component such as, ASR and SMT. The corpus has a total of 28,084 Amharic-English parallel sentences. To keep the dataset consistent, the text corpus has been further preprocessed, such as typing errors are corrected, abbreviations have been expanded, numbers have been textually transcribed and concatenated words have been separated. Amharic speech recognition is conducted using words and morphemes as a language model with a phoneme-based acoustic model. Similarly word and morpheme have been used as a translation unit for Amharic in Amharic-English machine translation. Morpheme-based segmentation of training, development, testing obtained by segmenting word into sub-word unit using corpus-based, language independent and unsupervised segmentation for using morfessor 2.
Similarly, language model data for Amharic speech recognition has been collected from different sources. A text corpus collected for Google project (Tachbelie and Abate, 2015) have been used in addition to BTEC SMT training data excluding the test data. Like speech recognition, a total of 42,134 sentences (374,153 token of 8,678 type) English language model data have been used for Amharic-English machine translation. The data is collected from the same BTEC corpus excluding test data. Consequently, corpus based and language independent segmentation have been applied on a training, development and test set of Amharic SMT data. Morfessor is used to segment words to a sub word units. Table  3  presents summary of the corpus used for Amharic-English machine translation using word and morpheme as a unit.
Experimental results Speech translation experiments are conducted through cascading components of speech translation as discussed in Section 1. In speech recognition experiments, Kaldi  (Povey et al. , 2011) , SRILM  (Stolcke et al. , 2002)  and Morfessor 2. 0  (Smit et al. , 2014)  have been used for Amharic speech recognition, language modeling and unsupervised segmentation, respectively. Morfessor based segmentation has been applied to segment training, testing and language model data for Amharic. In addition to this, Moses and MGIZA++ for implementing a phrase based statistical machine translation and Python is used for implementing the post-edit algorithm and to integrate ASR and SMT under the Linux platform. The entire ASR experiment is conducted using a morpheme-based language model with phonemebased acoustic model.
annotation files in the Gene File Format (. gff) to the SRD (Staphylococcus regulatory RNA Database)  22. In this study, the sRNA content of four strains was compared, and their predicted locations suggested that sRNA content is strain-dependent. It therefore seems that global analysis devoted to the identification of novel sRNA is still useful for many bacteria, including S. aureus. This was recently confirmed by studies conducted on multilocus sequence typing 8 (ST8) strains, with new sRNAs described in USA300 and NCTC8325  15, 21. Here, we combined bioinformatics and experimental procedures to identify and characterize novel sRNAs expressed by the S. aureus strain Newman  23. This is a methicillin-susceptible clinical isolate often used to study staphylococcal diseases in animal models  23  , and it belongs to the common ST8 clonal lineage.
Five candidates were located directly downstream or upstream of an already known sRNA. Altogether, our results strongly suggested the presence of additional sRNAs expressed in Newman strain. Experimental validation of 17 novel sRNAs expressed from core and accessory genomes. Using RNA-Seq data and after curation, the number of sRNA candidates was reduced considerably, from 48 to 17 IGRs. To avoid false positives, we needed to validate their expression by other means. Transfer-messenger RNA (tmRNA) was used as an internal control for northern blot analysis monitoring the expression of the 17 IGRs during growth in brain-heart infusion (BHI) medium at four time points (Fig. S1 ). In this way, the expression of 11 sRNAs was confirmed (Fig. 1A-K ), corresponding to the regions with the highest HTSeq counts in the RNA-Seq dataset (Table  1 ).
S2 ). The transcript length of each RNA was estimated using tmRNA (358 nt) combined with a prestain marker for small RNA (Fig. 1A-K ). For the most part, the calculated lengths were close to those estimated by DETR'PROK and/or by visualization using Artemis software. However, significant differences were observed for a few sRNAs. For instance, IGR_1141544 (Fig. 1D ) exhibited a strong northern blot signal for an approximately 150-nt long transcript with the presence of higher bands of weaker intensities. Similarly, northern blot revealed two RNA fragments for IGR_2839262 (Fig. 1K ). To assess whether the presence of additional bands was due to RNase enzymatic cleavages, contribution of RNase Y and RNase III  35  to a potential processing of IGR_2839262 was tested and rejected (Fig. S3 ). This suggests (i) that one or more other RNases (RNase J1 or J2, RNase R, etc.
To investigate this, and because so far the S. aureus RNAs transcribed from the opposite strand of a known sRNA are usually smaller than 100 nt  14, 22  , we performed a new RNA-Seq. In this one, we made and purified cDNAs with a minimal length of about 70 bp and generated single reads of 50 nt (sequenced using MiSeq), while the size of the smallest fragments generated for HiSeq sequencing was larger than 100 bp. We then used Artemis to specifically look for reads mapping on the opposite strand of these 13 RNAs, finding five additional read clusters (Table  3 ). Interestingly, we identified two of these (named as2 and as3) on the opposite strand of sRNAs expressed from IGR_1141544. Probes and primers were designed to verify whether transcripts could be detected by northern blots and RT-PCR.
Even though no bands could be observed by northern blot using various DNA probes, all transcripts were individually recovered by RT-PCR (using specific primers for reverse transcription). This suggested low expression levels of these RNAs under our experimental conditions (Fig. 2 ). Altogether, this increased the novel sRNA count to 22, and highlights the importance of combining several sequencing protocols and detection methods for thorough and exhaustive sRNA identification. Newman strain contains an unusual condensed sRNA cluster composed of five transcriptional start sites. In IGR_1141544, comparison of the transcript lengths determined by northern blots with the sRNA lengths either inferred from DETR'PROK or after Artemis visualization of single ends revealed major differences. While DETR'PROK predicted a single transcript of 424 nt (Table  1 ) and Artemis suggested a 650-nt one (Fig.
3A , P1 to P3). This revealed the presence of several RNAs transcribed from the positive strand in this nearly 700-nt region. Interestingly, northern blot conducted with probe P1 resulted in the detection of two bands of around 130 and 260 nt (Fig. 3B ). This suggested the presence of either two distinct transcription start sites (TSSs) or a single TSS with two transcription terminators. Sequence analysis using ARNold  42  supported the second hypothesis, revealing two Rho-independent terminators: one corresponding to the NWMN_1039-1 3′ end, and another located further downstream. Conversely, probes P2 and P3 identified single transcripts of around 140 and 310 nt (Fig. 3B ). Through RT-qPCR, we showed that levels of all three RNA (P1 to P3) were increased at OD 600nm 6 and 10 (Fig. 3C ), confirming the previously obtained northern blot results (Figs  1  and S2 ).
Extensive sRNA end-mapping and assignment of srn identifiers. We previously compiled staphylococcal sRNAs into the SRD database under simple srn identifiers to harmonize the sRNA repertoire  22. To generate names for these 24 newly discovered sRNAs, we first determined the 5′ and 3′ ends of the transcripts by RACE-mapping (Table  5 ). Then, we provided an srn identifier for each RNA (Table  5 ) based on its location on the genome of five reference strains (N315, Newman, JDK6008, NCTC8325, and USA300) as provided in SRD  22. The nucleotide sequences defined by RACE confirmed all of the previously estimated lengths, and provided additional proof that the two sRNA copies, hereby renamed Srn_9650 (IGR_1103623) and Srn_9660 (IGR_2017461), were transcribed. We also used TEX followed by polyphosphatase treatment to identify a TSS for srn_3765 (IGR_2083121) at position 2083472 (a 276-nt transcript), while an RNA fragment of only 204 nt was detected without TEX treatment, suggesting maturation of that particular RNA at position 72 (not detected by northern blot, Fig.
1I ). TEX-RACE-mapping of Srn_5075 (IGR 2839262) only revealed a 220-nt fragment, indicating that the upper band detected by northern blot is non-specific (Figs  1K  and S3 ). Using the accurate srn coordinates, we compared the 24 new sRNA sequences with sRNAs recently identified in USA300 and HG001  15, 21  and for which the srn annotation was not yet available. This showed that, among the 24, only three of them (srn_2335, srn_9345, and srn_4635) were already identified as transcribed in those two recent studies (Table  5 ). All the 24 transcripts identified in this report fit in the sRNA class, as the longest is shorter than 400 nt. As studies on sRNAs showed that some can contain ORFs  2, 11, 14, 21, 22  , we used ORFfinder to search for their presence within each mapped sequence. This unveiled that 13 sRNAs contained a small ORF (Table  5 ).
We then used them to BLASTP to assess whether they were automatically annotated as potential CDS within S. aureus taxon. This revealed that, among the 13 sRNAs that contained an ORF, 10 were predicted to encode a hypothetical small protein or peptide  in at least one S. aureus strain although transcription of these putative genes (and therefore translation) was not demonstrated. We also used the Uniprot database to search for sequence homology with known proteins or domains. Eight of them did not match any referenced proteins or peptides, while the two remaining shared partial homologies with larger proteins. Together, these data indicate that 14 of the identified transcripts identified are bona fide non-coding sRNAs, and that the features of the remaining 10, make them good candidates for sRNA with potential dual-functions.
The analysis of these new sRNAs in various S. aureus genomes showed significant differences in the distribution of the five sRNAs expressed from the srn_9342-9346 locus. Therefore, we compared the genetic organization of this srn_9342-9346 locus in Newman, NCTC8325, CA-347, Mu50, and JH1 strains, all from different lineages (Fig. 4 ) and representative of the genomic distribution in S. aureus of these five sRNAs in S. aureus. This revealed that Newman is the only strain that has this highly compacted srn gene cluster (Fig. 5 ). In Newman, the srn set is located at the 3′ end   21  ; c described in Fig. 3 , Table  3 , and Table  4 ; d described by Mader et al. 15. ORF of 30 codons or more were identified using ORFinder. Potential CDS were searched using BLAST within the non-redundant database.