dataesr/scientific-paragraphs-categorization huggingface dataset:
GeneFizz analysis of an E. cuniculi chromosome end (IIα) showing the relationship between open-closed transitions and recombination sites. A. An overview of a complete IIα organisation can be obtained by combination of different sequences (see bottom). CDSs are represented by boxes with arrows indicating their direction of transcription and are ordered from telomere (left) to chromosome core (right). Letters AE, B, C and D are applied to the members of the four multigenic families described in the present study. Name and Uniprot accession number are given for most CDSs. B. DNA of EXT repeats is getting melted at high temperature and offers an alternation of "open" and "closed" areas. Double stranded DNA ("closed" state) correspond to the null value. At the maximum, the DNA is fully melted, i.
Other directions to be taken are likely to involve the use of large online translation databases (e. g. Linguee, Tradooit) since these are considered as complementary to TM systems based on the survey among recent graduates. In particular, graduates view such resources as a "source of inspiration", shedding "new light on translation solutions" and providing "new clues". Integrating such resources will necessarily imply raising student awareness of the limits of such tools in terms of translation quality and assess their usefulness compared with other online resources.
Actually, there is no particular pressure from the market to train translators to use corpora (Frankenberg-  Garcia, 2015)  which, in turn, does not encourage the integration of corpora in translator training and may be "counterproductive" for teachers involved in the training of future translators. However, a number of surveys -though limited -has set out to uncover the use of corpora and corpus technology by professional translators and has shed some interesting light on the professional practice. To begin with,  Bowker (2004)  carried out a literature survey of publications produced by Canadian translators' associations and also analyzed a database of job advertisements to "determine how many Canadian employers are seeking candidates who are familiar with corpus-based resources" (ibid. , 213).
So it looks like defining what a corpus is as well as defining the corpus-related concepts should be part of the core programme of any translator training institution. Finally, respondents to Gallego-Hernández's survey among corpus users said they favoured specialized and multilingual corpora over general and multilingual corpora, as well as stable corpora. This echoes to our survey among recent graduates at the University of Grenoble Alpes who claimed that they widely used multilingual large online translation databases (Linguee ranked first in the results) and hardly resorted to DIY corpora. Be it the European Mellange survey or the Spanish one, corpus users acknowledge the benefits of corpora and require specific training, which calls for a greater collaboration between academic and professional circles.
Actually, conducting such experimental studies involves considering issues that particularly relate to: (i) defining among the different stages of the translation process which stage(s) to incorporate in the study i. e. understanding of the subject field, lexical, terminological, phraseological and syntactic accuracy -in relation with conceptual and linguistic information retrieval -as well as stylistic fluency and appropriacy; (ii) selecting the various corpora to be assessed and to be compared: DIY corpora (monolingual, comparable, parallel corpora), online corpora (for instance the BNC or the COCA for the English language), large online translation databases (e. g. Linguee, Tradooit or Reverso Context) as well as Google search; to our mind, this issue is of major importance as it has an impact on the corpora and corpus processing tools to integrate into the translation classroom based on their efficiency on student translations; (iii) determining the criteria to consider in order to measure the usefulness of the corpora under study (for instance, which linguistic criteria).
Conducting such experimental studies also involves devising an adequate methodology at the experimental level that includes determining (i) which instructions give students participating in the experiment -especially in terms of providing feedback on their search and on their use of electronic resources -and (ii) the linguistic items or portion of text to evaluate; both options have already been investigated as  Kübler (2016)  had her students translate 200 words while  Heylen and Verplaetse (2015)  focused on 10 isolated items (terms such as abdominal pain, adverse reactions and hepatitis enzymes). We have started addressing the aforementioned issues at the University of Grenoble Alpes in an experiment which sets out to assess the usefulness of corpora for translation, more specifically the usefulness of free online resources integrated in a course on tools and resources for translators (term banks, large translation databases and specialized search engines in particular) compared with the Scientext corpus, a free online specialized corpus in biology and medicine 18 which the students participating in the experiment have been trained to use for translation purposes.
Analyzing the translations consisted in comparing both translations taking into account the four criteria just mentioned. Overall, the analysis shows promising results with an added value of 12% between both translations as using Scientext helped students modify their 1 st translation (e. g. treatment duration / sedimentation velocity) which then either complied with the reference translation (duration of treatment) or proved to be enhanced (sedimentation rate). In contrast, the results also show a significant loss in translation quality as students seem to rely heavily on the Scientext corpus (for instance, ponctions d'abcès first translated by abscess punctures -the actual reference translation -was changed into abscess drainage as no occurrence of the former was found in Scientext). Generally speaking, this "blind faith" in the corpus needs to be tackled in the corpus-based translation classroom where the emphasis should be put on raising student linguistic awareness since using a corpus as a translation resource requires interpreting the data  (Kübler, 2016).
We performed a post hoc power analysis in which data from the initial dataset were shuffled to generate a simulated dataset, selected for genes with differential expression of |log2fold| ≤ 2. 0 (see Methods). This simulated dataset was analysed using the same approach as described for all other annotations. Our analysis revealed 2781 simulated genes with statistically significant differences in expression in this data set, suggesting the study was adequately powered to detect expression changes at a log2fold change ≤ 0. 6 (Fig. 2D  and Supplementary Figure  4 ). A similar post hoc power analysis was conducted on the miRNA data (Fig. 3D ) which identified 3 differentially expressed miRNAs, a proportion of differentially expressed miRNAs similar to our original analysis (0. 48% vs 0. 35% respectively) (Supplementary Figure  4 ).
The small RNA library preparation selected for RNA species with length 22-30 nt. Sequencing small RNA (smRNA) and RNA was performed on the Illumina HiSeq2500 (Edinburgh Genomics, Edinburgh, UK). Intended library size for total RNA was 37. 5 million single end reads of 125 bp and for small RNA was 20 million single end reads of 50 bp. Quality of the sequencing was verified using FastQC. Adaptor contamination was removed from the smRNA-seq libraries using trimmomatic to a minimum length of 25 nucleotides  38. SmRNA-seq reads were aligned to the Rattus norvegicus genome version rn5 using Butter with default parameters  39. Differential expression was determined using DESeq2  40. RNA-seq libraries were aligned to Rn5 using Star v2. 3. 0 41 on a custom-built splice junctions database based on Rn5 ensembl73 protein-coding annotations, with the maximum proportion of mismatches over the read length of 0.
05 and minimum mapping read length of 125 nts. As positive controls, liver and testis RNA-seq data from the Rat bodymap were subject to the same bioinformatic pipeline  42  (Supplementary Figures  3A, D ). All raw RNA-seq data, and processed count tables are archived at the Gene Expression Omnibus with accession number GSE80721. The code used to perform read quality control (pipeline_readqc. py) and short read alignment (pipe-line_mapping. py) can be found at https://github. com/CGATOxford/CGATPipelines.
Sample clustering and principal components analysis. Variance stabilising transformed (VST) read counts for protein-coding genes, miRNAs, repeats and piRNAs were calculated using DESeq2. Between-sample Pearson correlations (r) were calculated using VST counts and used to hierarchically cluster samples using average linkage clustering, with distances defined as 1 -| r |. Principal components analysis (PCA) was performed on scaled and centred VST counts using the R prcomp function. Sample clustering was visualised using the R gplots package function heatmap. 2, and PCA results were plotted using the R grammar of graphics package, ggplot2.
Differential expression testing. Uniquely aligned reads were counted over rn5 genomic annotations (ensemble v73 protein-coding genes, miRBase miRNAs  43  , piRNAQuest piRNAs  44  and repBase repeat classes  45  ) using featureCounts  46. Genomic annotations with a mean read count < 1 across all samples were excluded from analysis. This resulted in the differential expression testing of 18,025 protein-coding genes, 285 miRNAs, 7390 piRNA annotations and 522 repeat classes. Statistical testing was carried out separately for protein-coding genes, miRNAs, piRNAs and repeat elements. Differential expression testing was performed using a negative binomial general linear model, regressing genomic annotation read counts on diet, adjusted for library size, in the Bioconductor package DESeq2  40. P-values were calculated based on the Bayesian shrinkage moderated log2 fold changes by a Wald test with H 0 : log2 fold change = 0, H A : log2 fold change ≠ 0 and adjusted for multiple testing using the Benjamini & Hochberg procedure  47.
Power analysis. Evaluation of statistical power was determined by simulation. For any given annotation, read counts were randomly and iteratively shuffled to generate a simulated dataset of read counts, preserving the experimental design, i. e. counts were shuffled between different genes, but not different treatment groups. Shuffled annotations were retained with |log2 fold change| ranges 0. 0-2. 0 for protein-coding genes, and 0. 0-0. 5 for miRNA annotations. These ranges were selected to reflect the observed fold changes in the experimental data. The counts tables derived from the shuffled data sets were used as input into the same differential expression testing procedure described above. The shuffled counts tables were generated using the counts2counts. py Python script in the CGAT code collection  48  , found at https://github.
com/CGATOxford/cgat. To generate Figs 2D and 3D, the relevant table of simulated counts were spiked into the table of relevant annotation counts, and the differential expression analysis was performed on the combined table. Statistical power was calculated in bins of 0. 1 as the proportion of statistically significantly differentially expressed annotations, relative to all tested annotations of that class (i. e. either miRNAs or protein-coding genes). The R code to generate the power curves and all other code to perform differential expression testing can be found at https://github. com/MikeDMorgan/proj035, including the counts tables on which the power analysis in Supplementary Figure  4  was performed. qPCR validation of sequencing. cDNA was prepared using SuperScript VILO (Invitrogen) as per the manufacturer's instructions.
Acknowledgements Thanks go to  Will Mungal  for assistance with animal husbandry, to the  Fiona Rossi ,  Shonna Johnston  and  William Ramsay  at the  Queen's Medical Research Institute Flow Cytometry Facility  for assistance with FACS purification of GCs and to  Edinburgh Genomics  for RNA library preparation and RNA-sequencing. Thanks also go the  Robert Hammer of University of Texas Southwestern Medical Center  for supplying the GC-eGFP line. This work was supported by grant  G33253  to RMS from the  UK Medical Research Council (MRC) , by an  MRC   Clinical Training Fellowship  to TJGC and a  Scottish Senior Clinical Fellowship  ( SCD/09 ) to AJD. CGAT (MDM and AH) is funded by the  UK Medical Research Council  (grant number  G1000902 ). Thanks go to the other members of the  CGAT  programme for their continued development of the CGAT code collection.
Methods The study protocol was made available for review to ICU physicians from different countries. Once the ICU was enrolled in the research group, data were collected for all admitted patients for 3 consecutive months, in the period between April 2012 and September 2014 using an electronic case report form. Exclusion criteria were (a) age <18 years or >85 years; (b) chronic dialysis; (c) short-term postoperative admission; (d) life expectancy less than 48 h; (e) need for extracorporeal membrane oxygenation (ECMO) within the first 48 h of ICU stay. All types of ICUs were eligible on voluntary basis within the indicated period. Local ethics committees approved the study according to the local regulations.
Data collection We designed a specific password-protected website for the study. Centres could only access data related to their patients. An automatic data verification system screened each field for missing or out-of-range values and data inconsistencies, generating a visual alert. The online tool included seven sections: (1) Demographics, anthropometrics, and admission diagnoses; (2) Medical history; (3) Admission data; (4) Daily vital signs and laboratory values; (5) RRT;  (6)  Sepsis and (7) Outcomes/case closure. Data input was on daily basis. Severity of illness was defined by Simplified Acute Physiology Score II (SAPS II)  [12] , Sequential Organ Failure Assessment (SOFA)  [13]  and Acute Physiology and Chronic Health Evaluation II (APACHE II)  [14]  scores at admission and described in a pie chart (Additional file 1: Figure  S1 ).
AKI diagnosis was based on creatinine (Cr) levels using the KDIGO classification  [15]. The creatinine value prior to hospitalization (Medical history) was used as baseline. If missing, a reference creatinine was requested utilizing the Modification of Diet in Renal Disease (MDRD) formula assuming a glomerular filtration rate of 75 ml/ min/1. 73 m 2. Daily data entry included serum creatinine, total fluid intake and output, urine volume, ventilation modality, diuretic therapy and, if present, RRT. As soon as a patient met the criteria for AKI, a full data entry section was activated for 14 days with automatic daily display of AKI stage. Data for SOFA calculation and for epinephrine/norepinephrine dose were additionally included. A dynamic chart displaying fluid balance and clinical management was generated using the data entered daily (see Additional file 1: Figure  S2 ).
The RRT section included data on modality, flows (blood, dialysate, reinfusion, pre/post ratio, and net ultrafiltration rate), anticoagulation, duration (for each 24-h period), reason for initiation, type of vascular access and malfunctions. RRT fields were automatically adjusted according to treatment modality. It was mandatory to report the cause of downtime whenever the effective treatment time was less than that prescribed. Data on circuit life span were recorded for each treatment. The case closure section included data on outcome and renal status at discharge. Data on fluid balance were automatically calculated by the CRF from the time of admission, based on daily data entry.
Acknowledgements The authors are grateful to  Inga Bayh  1 and Katharina Brand 1 for the statistical support,  Matteo Recchia  2 and  Giovanni Aneloni  2 for their invaluable assistance with the information technology, and  Fabio Grandi  1 for helping with the interpretation of the results. Other investigators M. Balciunas 3 , B. S. Oliveira 4 , L. Cachafeiro 5 , E. G. Porcile 6 ,  M. Ranieri 7  ,  V. Cantaluppi  8 , V. Schweiger 9 ,  L. Montini 10  ,  M. Gurjar 11  ,  C. Liuzzo 12  ,  N. Brienza 13  and  J. Silvestre   14. 1  Fresenius Medical Care ,1 Else-Kröner-Straße. 61352  Bad Homburg -Germany. 2International  Renal Research Institute of Vicenza (IRRIV) ,  San Bortolo Hospital, Vicenza, Italy.
To provide additional evidence that TMEM-218 is specifically associated with the MKS module, we assessed whether its TZ localisation is perturbed upon disruption of a"core" MKS module protein. Indeed, TMEM-218 is no longer present at the TZ in the mks-2 MKS module mutant (Fig  2B ). In contrast, and as expected for an MKS module protein, TMEM-218 remains correctly localised in the nphp-4 core NPHP module mutant (Fig 2B ). Reciprocal experiments were performed to query if TMEM-218 is itself required for the localisation of other TZ proteins. Our data show that TMEM-218 is not required for the localisation of MKS-5, MKS-2 ("core" MKS module protein), or NPHP-4 ("core" NPHP module protein) (Fig  2C ). Overall, our findings point to a specific association between TMEM-218 and MKS module components, potentially as a more "peripheral" TZ component compared to other "core" MKS module proteins, which include MKS-2, MKSR-1, MKSR-2, and TMEM-231 (Fig  2D )  [25, 28, 30, 31].
The TMEM17 variation fully segregates with the disease in the family (Figs 6A and S3A) and is not detected in 150 in-house controls or in over 73,000 samples from the public databases EXAC and EVS. Furthermore, the p. N102K variant is predicted to be pathogenic by four distinct bioinformatic predictors (PolyPhen2, SIFT, Mutation Assessor, Mutation Taster) and affects a highly conserved amino acid residue (S3B Fig). Importantly, we show that fibroblast cells isolated from a TMEM17-mutated sibling display a much reduced ability to form cilia compared to cells obtained from the healthy heterozygous mother (Fig 6B  and 6C ). Together, the discovery of a homozygous mutation in a highly conserved residue of Tmem17 that is not present in control genomes and is correlated with a defect in ciliogenesis provides strong evidence that disruption of Tmem17 results in a ciliopathy, OFD6.
Notably, the C5orf42 gene was previously thought to be the main cause of OFD6  [61] , although this is now debated  [62] ; and a second gene related to TMEM17-TMEM216 (MKS2) -has also been associated with OFD6  [63]. We therefore sought to identify additional genes that encode CEP-290-dependent TZ proteins that may be mutated in OFD6 patients. We identified individual families with missense mutations in two genes, TMEM138 (homozygous p. M118L) and TMEM231 (compound heterozygous p. P179A/p. P219L) (Figs 6D, 6E and S4; S2 Table ). All mutant alleles are predicted to be pathogenic in that they affect conserved residues, are not found in control cohorts or public databases, and fully segregate with the disease in the respective families (Fig 6D  and 6E ). Collectively, our findings provide strong evidence that Tmem17 is a novel OFD6-associated protein that is necessary for ciliogenesis and suggest a potentially expanded spectrum of TZassociated proteins (Tmem138, Tmem231) linked to this JBTS phenotype.
CEP-290 Localisation Volume Analysis All images were taken on a spinning-disc confocal of phasmid (PHA and PHB) cilia, as described above, with the same settings for the same exposure time (600 ms). CEP-290::GFP localisation volumes were detected by the Volocity software "find object" program with an automatic threshold offset of 200. p-values were calculated by Dunn Kruskal-Wallis multiple comparison (Holm-Sidak method).
Quantification of Transgene Expression Levels in Phasmid Neurons Anaesthetized L4 stage transgenic worms in 10 mM levamisole were prepared for spinningdisc confocal microscopy. Fluorescence in at least 20 pairs of cell bodies of PHA and PHB neurons from strains expressing TMEM-218::GFP, TMEM-231::GFP, or CDKL-1::GFP (isoforms A and C) was visualized and analyzed using Volocity software (Perkin Elmer). To obtain relative fluorescence intensity for each strain, the background fluorescence signal was subtracted and the absolute fluorescence intensity measurements were divided by the median fluorescence intensity in wild type. The relative fluorescence intensity data were plotted using Box and Dot plots in R. The assessment of normal distribution of each dataset was done using the Shapiro-Wilk test.
The p-value of the dataset that shows normal distribution was obtained by Tukey multiple comparisons of means of 95% family-wise confidence level. The p-value of the dataset that shows non-normal distribution was calculated using the Kruskal-Wallis test; Dunn Kruskal-Wallis multiple comparison (Holm-Sidak method) was used for comparing more than two groups. Results are presented in S1D-S1G Fig. Quantitative RT-PCR to measure CEP-290::GFP and cep-290 transcript levels was performed as previously described  [68]  using iTaq Universal SYBR green supermix (Biorad), an Applied Biosystems StepOne real-time PCR system, SuperScript III reverse transcriptase (Invitrogen), and the following primers: control, tba-1 forward: tcaacactgccatcgccgcc and reverse: tccaagcgagaccaggcttcag, GFP forward: atggtgttcaatgcttctcg and reverse: tgtagttcccgtcatctttga, and cep-290 forward: tgctcagcgagttgaatagg and reverse: aagcttcccaatttgctcat.
Bioinformatic analyses were conducted to filter data and remove low-quality calls as well as variants with a frequency of >1% in public databases dbSNP ver. 141 (http://www. ncbi. nlm. nih. gov/SNP/) and Exome Variant Server (http://evs. gs. washington. edu/EVS/) or found in internal controls. The identified mutation in the TMEM17 gene (NM_198276) was validated using bidirectional Sanger sequencing using the Big Dye Terminator chemistry (Life Technologies) and was searched against public databases dbSNP and Exome Variant Server. Potential pathogenicity was predicted using prediction software PolyPhen-2 ver. 2. 2. 2 (http://genetics. bwh. harvard. edu/pph2/) and SIFT (http://sift. jcvi. org/). Nomenclature was assigned according to the Human Genome Variant Society (http://www. hgvs. org/mutnomen/).
Sanger sequencing was also used to birectionally sequence all coding exons and exon-intron boundaries of TMEM218 in a subset of 160 Joubert patients who had tested negative at the previous NGS experiment. Probands from two large families diagnosed with OFD6 directly underwent whole exome sequencing. Three micrograms of genomic DNA per individual were subjected to wholeexome capture and sequencing using the SureSelect Human All Exon V5 kit (Agilent). The resulting libraries were sequenced on a HiSeq 2000 (Illumina) as paired-end 76 bp reads. BAM files were aligned to a human genome reference sequence (GRCh37/hg19) using BWA (Burrows-Wheeler Aligner; v0. 6. 2). All aligned read data were subject to the following steps: (1) duplicate paired-end reads were removed by Picard 1. 77, and (2) indel realignment and (3) base quality score recalibration were done on Genome Analysis Toolkit (GATK; v2.
1-10). Variants with a quality score >30 and alignment quality score >20 were annotated with SeattleSeq SNP Annotation (http://snp. gs. washington. edu/SeattleSeqAnnotation138/). Rare variants present at a frequency above 1% in dbSNP 138 (http://www. ncbi. nlm. nih. gov/SNP/) and the NHLBI GO Exome Sequencing Project or present from 312 exomes of unaffected individuals were excluded (http://evs. gs. washington. edu/EVS/). To improve exome analysis, data are crossreferenced with a list of known cilia-related genes from Ciliome Database, Cildb v2. 1 (http://cildb-archive. cgm. cnrs-gif. fr/) and transcriptomic study of cilia  [72]. The analyses were focused on genes with homozygous variants in consanguineous families and with two heterozygous variants in other cases, prioritising (1) genes associated with human pathology in Clin-Var (http://www.