dataesr/scientific-paragraphs-categorization huggingface dataset:
2008). Altogether, these examples highlight that pieces of RNA presenting sequences that are compatible with Hoogsteen rules might have a widespread role in epigenetically regulating a large number of genes. We chose to work on the simple genetic model Drosophila melanogaster because of the availability of genome annotation information with substantial descriptions of mutant phenotypes in multiple databases. We investigated whether the two classes of RNA (lncRNA and mRNA) sheltered compatible sequences to engage the triplex structure with DNA, raising the idea that they might have a widespread role in chromosome 3D architecture and gene regulation. lncRNAs are known to specifically and restrictively recruit promoters, resulting in downregulation or overexpression of targets genes  (Mercer et al.
Triplex identification There are several algorithms dedicated to the identification of triplexes. Triplexator  (Buske et al. 2012)  is the first computational framework that integrates all aspects of triplex formation. It allows the detection of triplex-forming oligonucleotides (TFOs), triplex-target sites (TTSs), and the localization of TFO:TSS matchings that are potential sites for triplex formation. LongTarget  (He et al. 2015)  includes similar features plus the ability to determine the best TFO:TTS matchings and to evaluate triplex stability. Unfortunately, the software is no longer available for download. Trident  (Paugh et al. 2016 ) is dedicated to the identification of microRNAs favoring triplex formation. The tool cannot be used to detect TFOs in long RNA sequences. In our study, we used Triplexator, which has all the features that we needed.
The program Triplexator was also used to process the FASTA sequences of the chromosomes, genes, and exons in order to identify TTSs and to detail their distribution. For this search, no mismatches were allowed, and a minimum length of 20 bases was also used. Using these parameters, the program identified 9702 target sites on the chromosomes. Potential triplex sites were also computed with the Triplexator program using perfect matches between TFOs and TTSs and a minimum length of 20 bases. Normally, each TFO was potentially able to form a triplex with the TSS located at the same position on the complementary strand. These systematic auto-matches did not bring anything to the study of the importance of the TFO-TSS matches on a genome-wide scale. We decided not to take into account these self matches.
2014). The EAGLE algorithm  (Shen et al. 2009)  in ClusterViz was used to identify three network modules containing 8-34 genes. The EAGLE clustering method, included in ClusterViz, was able to find spanning clusters, meaning that in our case, some genes were common to several clusters. Gene Ontology enrichment analyses were performed on the list of genes composing these modules and used to annotate the graph presented in Figure  3.
Chl-a concentrations were calculated from spectrophotometric measurements on acetone extracts using a monochromatic method with acidification  [27]. In addition, precipitation data was obtained through the Hydrometeorology Service of the Regional Council of Bizkaia (http://www. bizkaia. eus/ Ingurugiroa_Lurraldea/Hidrologia_Ac/Datos_meteo. asp?Idioma=CA&Tem_Codigo=2679).
16s rRNA amplification and sequencing The 16S rRNA samples were amplified and sequenced by the Next Generation Sequencing Core at Argonne National Laboratory, Lemont, IL (USA) (http://www. earthmicrobiome. org/). Earth Microbiome Project's  [28]  protocols  [29]  were followed for the amplification and sequencing of the community 16S v4 region by using 515f and 806r primers that contained 12 bp barcodes for sequencing. The sequencing was carried out in two MiSeq runs (2x150 pairedend). The data is available in the QIITA portal (ID 10470) and on the ENA database (study: PRJEB14094).
Bioinformatic pipeline The raw sequences were trimmed using Sickle tool (v1. 33)  [30]  with default parameters (including Phred score ! 20). Next, Pear software (v0. 9. 6)  [31]  was used to merge Illumina paired-end reads, using a cut-off of 0. 01 (P-value) for the observed expected alignment score. Next, we utilized fastq-barcode. pl [32]  to remove non-existent barcodes from the fastq achieved by Pear. Before carrying out the taxonomic assignment, the chimera sequences were removed by identify_chimeric_seqs. py in QIIME using the usearch61 (v7. 0. 1090) de novo method  [33, 34]. The resulting dataset was then analyzed by QIIME software (v1. 9:  [35] ). We only included sequences that were 240-260 bp in length (average 253bp) to avoid background noise in the subsequent analyses. An open reference OTU picking method was used in QIIME for clustering using a 97% similarity cut-off using UCLUST algorithm (v1.
2. 22q)  [33]  and the taxonomy of the reference sequences was assigned based on Silva 119 database version  [36]  (clustered at 97% identity). The OTUs with which representative sequences failed in PYNAST alignment were discarded. After the taxonomical assignment, all chloroplast were removed from the BIOM file using filter_taxa_from_otu_table. py script in QIIME. Afterwards, samples with less than 5000 sequences were eliminated. Then, all OTUs with less than 10 sequences were removed. Finally, the BIOM file was normalized using metagenomeSeq's CSS algorithm, which normalized sequences using the cumulative sum scaling transformation  [37].
Community composition analysis First, alpha diversity analysis and taxonomic assignment were conducted to determine the community richness and composition. The alpha diversity (observed OTUs and Shannon) of the samples were calculated using phyloseq (v1. 14) R package  [38]. To visualize the bacterial community composition of the samples, taxa_summary_through_plot. py command in QIIME v1. 9 software  [35]  was used. In order to determine water mass specific OTUs as well as the OTUs that were present in all water masses, the core microbiome was analysed using compute_core_microbiome. py command on QIIME v1. 9  [35]. The core microbial communities were defined as the OTUs that were present in 100% of the samples within each water mass (IS, MS, OS, B30, B33, B35) throughout the year. To compare the shared OTUs between the different water masses, a Venn diagram tool was used (http://bioinformatics.
Finally, to understand the bacterial dynamics in the inner euhaline zone of the Estuary of Bilbao, where the low DO concentrations and high values of temperature and chlorophyll concentrations dominate in summer, we used extended Local Similarity Analysis (eLSA) software  [44]. The analysis was performed using OTUs with highest abundance values at B30 samples. Following eLSA software guidelines, a total of 85 OTUs were included in the analysis. To adapt to the algorithm limitations and minimize computational cost, eLSA was used to reveal statistically significant local and potentially time-delayed association patterns between OTUs and environmental factors. Normalization of variables was performed by 'robustZ' method, including 14 time spots for the total number of sampling months. The rest of the analysis settings were set to default.
Lastly, q-values were calculated to determine false-discovery rates. Correlations with q<0. 01 were visualized in Cytoscape v3. 2. 1  [45] , creating a continuous mapping-based network. To identify the differences in OTU composition between water masses, a Kruskal-Wallis non-parametric test was carried out between tributaries and estuarine water masses. In this way, the OTUs whose abundances significantly differed between water masses were identified. Moreover, the community dissimilarity within the estuary and its tributaries were determined using a Detrended Correspondence Analysis (DCA) carried out by phyloseq (v. 1. 14) R package  [38].
The matrix of the variables was normalized by 'robustZ' method. A network was created with Cytoscape software  [45]  using the significant (q < 0. 01) correlations obtained in the eLSA analysis. The directionality of the relationship is marked with arrows with its temporal delay (in months) in the edge label (green) and the relation type between them positive (red) or negative (blue). https://doi. org/10. 1371/journal. pone. 0178755. g005 When analyzing the samples from the Estuary of Bilbao together with the two tributaries, the communities were grouped first by salinity and then by season (Fig  7 ), driven by precipitation and temperature changes. Freshwater masses (IS, NER, GAL) were further divided into three clusters: A) Galindo samples' community that clustered apart, B) freshwater spring and C) freshwater summer (Fig  7 ).
Funding Direction Générale de l'Armement (DGA); Russian Foundation for Basic Research (RFBR: 16-52-150006); ITMO University through the Government of the Russian Federation (Grant 074-U01). T. M. and A. K. acknowledge support from IRCICA (USR 3380 Univ. Lille -CNRS), from the ANR TOPWAVE (ANR-13-JS04-0004) project, from the "Fonds Européen de Développement Economique Régional", the Labex CEMPI (ANR-11-LABX-0007) and Equipex FLUX (ANR-11-EQPX-0017) through the "Programme Investissements d'Avenir".
Study subjects and serum samples Serum samples and clinical data were collected from participants in BUILD-3 (NCT 00391443), a multicentre, prospective, randomised (2:1), double-blind, placebo-controlled, event-driven, morbidity-mortality trial of bosentan in adults with IPF of less than 3 years' duration  [11]. Approval was obtained from all relevant ethics committees and institutional review boards before study start, and patients provided written informed consent. Serum samples were collected as part of the clinical protocol at baseline, 4 months and EOS. In total, 347 patients with IPF consented to serum sampling: 113 patients in the placebo arm and 234 patients in the bosentan arm. After collection, serum samples were aliquoted and frozen at -80°C until use. Samples that had signs of haemolysis were excluded for multiplex and singleplex assays, thus samples from 342 patients (113 patients in the placebo arm and 229 patients in the bosentan arm) were analysed in the multiplex assays.
Acknowledgements The authors thank  Geoffroy Bourquin ,  Annette Trebaul ,  Bérengère Renault ,  Solange Meyer ,  Hervé Farine ,  Julie Hoerner ,  Daniel Strasser ,  Oliver Peter  and  Peter Groenen  for technical support, and  Martine Clozel  and  Beat Steiner  (all  Actelion Pharmaceuticals Ltd, Allschwil, Switzerland ) for helpful discussions and encouragement. https://doi. org/10. 1183/23120541. 00074-2016.
Identification of proteins by mass spectrometry and bioinformatic analyses Then, proteins were identified by mass spectrometry (LC-MS/MS) and bioinformatics after tryptic digestion performed at 4°C, after separation by 1D-E or in solution. A detailed description of the parameters used for MS analysis can be found in  [29] [30] [31] [61] [62] [63]. For B. distachyon, the genomic sequence data were used  [64, 65]. For sugarcane, the SUCEST translated EST database was used  [66]. The amino acid sequences of the identified proteins were systematically compared to those of Sorghum bicolor, the closest related species having a fully sequenced genome  [64]. In case of partial EST sequence, this comparison allowed the bioinformatics prediction of sub-cellular localization and functional domains. For both plant species, the bioinformatics analysis of the identified proteins was carried out de novo in the same way regarding the prediction of their subcellular localization and of functional domains using the ProtAnnDB annotation pipeline  [67, 68].
Acknowledgements This research was partially funded by  USP-COFECUB (Proc. 10. 1. 1947. 11. 9 ), BIOEN/PRONEX FAPESP (Proc. 2008/56100-5 ),  INCT do Bioetanol , CNPQ (Proc. :  142784/2007-9 ), and  FAPESP (Proc. 2007/59327-8  and  2012/1212521-2 ). The authors are thankful to  Dr. R Pont-Lezica  who was at the origin of the projects. They also thank  Dr MC Falco  and  CTC  for providing the sugarcane plants and cells. They thank  Dr GM Souza  and  Dr M Nishiyama  for providing the SUCEST sequences. MJR, JGF, and CAL are thankful to the  Max Feffer Laboratory of Plant.
g. neutron transport, provide results in agreement with experimental data. These tasks are normally performed with software codes. Thus, they could be easily automatized, facilitating their generation to evaluators and their usage to users. In this direction, there are different projects ongoing, as summarised in Ref. [3]. Here we highlight those which are open to public (upon demand): ADVANCE  [4]  and MyENDF  [5]. Both systems launch automatically a set of processing codes on files submitted, and then parse the processing code outputs to inform evaluators/users about the quality of the files, remarking relevant issues identified by the codes. At NEA (Nuclear Energy Agency), the NDEC (Nuclear Data Evaluation Cycle) platform is under development. It aims at automatizing the tasks of testing, verification and benchmarking of nuclear data evaluations, as well as those systems mentioned before.
Processing It means to reconstruct and convert evaluated data stored in ENDF-6 format into different formats, considering the needs of the nuclear data users. Taking JEFF as one of the main NDEC users, the selected output formats are: • HENDF (Hybrid ENDF): A merge of the original evaluated ENDF-6 file with its PENDF (Point-wise ENDF), taking the reconstructed cross section (MF3) in the PENDF file to substitute the ones in the original ENDF files. This format is used to update JANIS  [7]  database. • GENDF (Group-wise ENDF), where cross section data from the ENDF-6 file is collapsed into a group-wise energy structure using a flat neutron spectrum. • BOXER: Corresponds to a format for storing covariance data for cross sections. These files are then used with JANIS to update its database of nuclear data libraries.
Verifications They consist in checking that format is correct and main physical constraints are fulfilled, not only in the original ENDF-6 format files but also in derived files such as PENDF (Point-wise ENDF) files. This task is partially carried out with processing codes, while further verification requires comparisons between evaluated data, and also against experimental data. The codes currently used for this tasks are ENDF Checks & Utilities (C&U) codes  [11] , PREPRO-2012 and NJOY2012. 50. For nuclear data comparison are considered Refs. [12]  and  [13] , both of them using identification of outliers using statistical estimators. However, they are not implemented yet.
NDEC Proof-of-Concept The Proof-of-Concept (PoC) of NDEC is based on defining the sequences of processing codes to automatize, so the processing, verification and benchmarking are accomplished. They are implemented in scripts, so they can be tested and analysed individually by running them on desktop or cluster computers. Two sequences are currently implemented: • Sequence 1 to generate ACE and GENDF format files from evaluations in ENDF-6 format. It makes use of ENDF C&U codes and PREPRO-2012 for verification of the ENDF-6 file, while NJOY2012. 50 is used for processing and generating the processed files. A general overview of the sequences is presented in Fig. 1 , where the big arrows indicate the order in which codes are run. The parameters and modules used for NJOY2012. 50 have been selected by Processing and Verification JEFF group  [16].
Example of usage within JEFF The JEFF project is currently working on the JEFF-3. 3 nuclear data library release. That involves the selection and assessment of evaluations, mainly neutron-induced data. In addition, in order to test the evaluations, it is necessary to process the ENDF-6 evaluations into formats readable by applications. Hence, NDEC can provide support to JEFF by • processing the files into different formats (see Sect. 2. 1), • verifying the files with processing codes (see Sect. 2. 2). That also leaves evaluations ready for benchmarking. The results from NDEC are provided per evaluation, as shown in Fig. 3 , that means all the results have to be checked. However, in order to improve the checking of NDEC results, a HTML webpage has been developed. It summarises in a table all the evaluations submitted for a given release and also includes the number of warning/errors encountered during the processing and verification for every evaluation.
A colour legend is applied in case errors are found (red), just warnings (yellow) or nothing (white). An example of this summary is given in Fig. 7. Because the table is sortable, JEFF community can focus first on those evaluations with errors and then on those with warnings. Thanks to NDEC, a JEFF release includes more data, apart from the ENDF-6 format files (evaluations): • ACE files with their XSDIR file, tested with MCNP. • HENDF files with their corresponding INTER and BOXER files. • JANIS database with all evaluations, for their visualisation in JANIS. • NJOY input files, for ACE file generation.
Conclusions At NEA, the NDEC (Nuclear Data Evaluation Cycle) platform is being developed. It is aimed at automatizing the processing, verification and benchmarking of nuclear data evaluations. So, it will help evaluators and users to speed up the assessment of evaluations and the processing into other formats. Its implementation is based in two approaches: Finally, NDEC PoC is currently being used for supporting JEFF-3. 3 beta releases. It provides processed files ready to be used for benchmarking and JANIS visualisation, and a summary of warning/error messages found during processing which helps JEFF community to identify issues with files.
Increased enzymatic activity of IDO1 associates with advanced MF stage The concentration ratio of KYN to Trp has been used widely as an indicator of IDO1 enzymatic activity. Here, we evaluated its correlation to MF disease stage. First, we investigated the necessity of overnight fasting and collected two partly overlapping sets of patient sera, fasting and non-fasting. We did not detect any significant effect of fasting on the KYN/Trp ratio (data not shown) and, hence, all sera were included in the final analysis. Overall, compared with healthy sera, the concentration of Trp was decreased in both, LyP and MF (Figs. 6C, S2A  and B ). However, only in MF the serum KYN/Trp ratio was increased significantly (p D 0. 012, (Table  1 ), while it did not differ in LyP (Fig. 6A ). Elevated KYN/Trp ratios were found more often in advanced than early stages of MF (Table  1 ).
Interestingly, our data showed that TDO expression exceeded that of IDO1 in LyP and ALCL. These results warrant further study on the role of TDO in these CTCL subtypes. In hematologic and lymphoid malignancies, high IDO1 expression was shown to associate with inferior survival and adverse clinical outcome. 6, 26 Because our observation on local KYN abundance correlated with IDO1 expression, we randomly selected a cohort of LyP and MF patients under followup or treatment, measured the serum concentrations of Trp and various KYN pathway metabolites and used the KYN/Trp ratio as an indicator of IDO1 enzymatic activity. KYN/Trp ratio was significantly increased in sera of MF patients compared with healthy controls and associated with advanced disease. Hence, the serum KYN/Trp ratio may serve as a potential prognostic biomarker for progressive MF but this will require validation in a large-scale prospective study.
After washing, the membranes were incubated with anti-rabbit HRP-conjugated secondary antibody (cat#PO448, Dako) or with anti-mouse HRP-conjugated secondary antibody (cat#AP308P, Millipore). Proteins were detected using the Clarity ECL reagent (cat#170-5061, BioRad). TDO2 and IDO1 expression values were normalized against b-actin using the ChemiDoc MP Imaging System and adjusted density values were calculated using the Image Lab software (Bio-Rad).
Acknowledgments We are indebted to  Mrs. Alli Tallqvist ,  Kaija J€  arvinen, and Nadine N€ urnberger for their skillful technical assistance,  Dr. Pilar Garc ıa Muret  for providing the FFPE tissues of six Spanish LyP patients, and  Prof. Dr. Robert Gniadecki  for providing the MyLa2000 cells.
Funding This study was supported by funding from  Instrumentarium Science Foundation (PM) ,  K. Albin Johansson  foundation (PM), Emil Aaltonenfoundation (PM),  HUCH Research Grants (AR) ,  Finska L€  akares€ allskapet (AR, PM), and the  German Research Society  ( KFO262 , KD, PJO). MEK is supported by  Drs. Martin and Dorothy Spatz Charitable Foundation.
The box 'TAA' indicates the position of the stop codon. C Kinetics of [ 35 S]-peptidyl-tRNA hydrolysis in the presence of eRF1 and eRF3a (black circles) or eRF1, eRF3a and UPF3B (white triangles). Termination reactions were assembled as in (B). A value equal to 1 corresponds to the maximum value for peptide release triggered by eRF1 and eRF3a. Data points show the mean of three experiments AE SEM. D UPF1 in vitro phosphorylation by SMG1-8-9 in the presence of UPF2L and/or UPF3B and in the presence (lanes 7-12) or absence (lanes 1-6) of the eRFs. In lanes 13-16 UPF2L and/or UPF3B were added after UPF1 phosphorylation. Samples were analysed by SDS-PAGE, Coomassie-stained to control for equal loading (lower panel) and autoradiographed (upper panel). SMG1 autophosphorylation (P-SMG1) confirms equal SMG1-activity in all samples.
Acknowledgements We are much obliged to  Tatyana Pestova  for generously introducing KK to the reconstituted in vitro translation system and for providing the MVHC-STOP plasmid. We thank  Hervé Le Hir  for the UPF1L and UPF1DCH expression plasmids and providing protocols for their production as well as for the ATPase assay,  Ludmila Frolova  for providing the plasmid pET15b-eRF3a, and  Stephen Cusack  for plasmids pPROExHtb_UPF2 ( 121-1227 ), pPROExHtb_UPF3B-N, and the RNA oligonucleotide for RNA interaction assays. We thank  Elena Alkalaeva  for providing Met-tRNA and the plasmids for initiation factors used for in vitro translation,  Alain Miller  ( Calbiotech S. A. , Mons, Belgium ) for providing HeLa cytoplasmic extracts,  Jan Medenbach  for the gift of SXL, and  Dmytro Dziuba  for the gift of IRP1.
Results Patient population and data collection. Among the 246 patients included in clinical trials, 180 patients had a full biomarker measurement profile and were assessed, i. e. 43 (17 and 26), 57, 32, and 49 patients for "PC" studies, "PC CsA", "PCNR" and "RIPOST-MI" studies, respectively (Table  1 ). Therefore the three full data sets, i. e. with CK, cTnI and CK-MB, included 132, 132 and 49 patients, respectively. Learning and validations subsets for CK and cTnI included 84 and 48 patients, respectively, whereas 32 and 17 patients were assigned for CK-MB, respectively.
064). The strong similarity of results obtained in learning and validation subsets shows that our models are able not only to describe biomarker (i. e. CK, cTnI and CK-MB) kinetic data (learning subset), but also to predict such kinetics of data, which were not involved in model building and parameter estimation. Our models should therefore be reproducible in other studies and suitable to estimate individual kinetic parameters and AUC of biomarkers of new patients and/or new studies without model building or parameter distribution estimation steps. Kinetic modelling provided lower p-values for association of biomarker with conditioning therapies, AAR and age, showing higher statistical power compared to general linear modelling (ANCOVA). This approach should provide accurate infarct size assessment in STEMI clinical trials and may open the way to trials with reduced numbers of subjects in a close future.