dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In summary, we utilized the strengths of existing network/pathway tools and databases to gain insight into processes related to cardio-myopathy have distinct molecular interaction patterns visible from various systems levels, including gene (microarray analysis), gene set, molecular pathway, and gene networks have shown that cardio-myopathy could involve three pathways; FA metabolism, angiogenesis/apoptosis and structure. Discriminating between the three pathways can help to improve the understanding of a drug's mechanism and further improve targeting in therapeutics drug research.
Results We conducted hierarchical regression analyses with SPSS 20 to test our hypotheses. Table  2  presents the results of the analyses predicting intention to use e-government for both services. In block 1, we entered the control variables (i. e. , gender, age, education, income, Internet self-efficacy, need for government services, and government staff). In block 2, we added the information quality characteristics (i. e. , accuracy and completeness), the channel characteristics (i. e. , convenience and personalization), and the means of uncertainty reduction (i. e. , transparency and trust). Finally, in block 3, we added the interactions terms (i. e. , Transparency × Accuracy, Transparency × Completeness, Transparency × Convenience, Transparency × Personalization, Trust × Accuracy, Trust × Completeness, Trust × Convenience, and Trust×Personalization).
T4 RNA Ligase 1 can only use 5' monophosphorylated RNA as substrate for ligation, andassuming that the XRN1 digestion was completeit is therefore only in the "+RppH" pool that ligation can take place. However, certain secondary structures resulting in recessed 5'-ends will protect RNA from XRN1 digestion (e. g. 5S rRNA and tRNAs). The "-RppH" pool therefore serves to determine the background level of ligation, and avoid false positives. For a given position on the genome, a large number of reads from pool "+RppH" that map to the position is thus indicative of a TSS, but only if the same position exhibits a low (or non-existent) number of reads in the corresponding "-RppH" pool. A betabinomial statistical test was employed in order to provide a consistent comparison between corresponding + RppH and -RppH datasets, and determine whether the number of reads mapping at a given position on the genome in the "+RppH" pool is significantly higher than the number of reads obtained in the "-RppH" pool.
The process results in a p-value that reflects our confidence in the genomic position to be enriched in the + RppH pool of the biological replicate. k The p-values of all the biological replicates are combined into a single p-value with Fisher's method. l and m To correct the p-values for multiple testing across all genomic positions, the false discovery rate (FDR) is evaluated and only those with a FDR ≤ 0. 01 are considered to be TSSs. Note also that for the FDR is only calculated for genomic positions with at least 5 detected ligation-events in at least one of the + RppH pools (UMI ≥ 5). n The TSSs then enter an annotation process that retrieve their surrounding sequence and downstream ORFs. TSSs separated by less than 5 bp are clustered together. Finally, to draw a global picture of operon structures, an independent detection of transcription terminators is operated with the software TransTermHP  [39].
The method can easily be applied to different bacteria and, because it allows quantification of transcription start sites via the UMIs, it is an ideal tool to compare different growth conditions of a given bacterium. It is our conviction that the data presented here will be highly useful for the scientific communities that study S. aureus, S. epidermidis, A. baumannii and E. aerogenes. In addition, the TSSs and operons defined by TSS-EMOTE will be highly useful as training sets for large-scale in silico operon prediction efforts such as the DOOR 2 Database of prOkaryotic Op-eRons, where experimental input data improves accuracy significantly  [10, 49]. As a consequence of the abovementioned advantages of TSS-EMOTE, we expect that in the near future, many prokaryotes as well as eukaryotes will be examined by TSS-EMOTE to answer the many biological questions that TSS analyses can illuminate.
In silico TSS identification The raw sequence read data from the Illumina sequencing was converted to EMOTE table-format according to the principles described in  [18] , using the EMOTE-conv software package  [51] , which lists all genomic positions for which an RNA 5'-end has been detected, with the number of Illumina reads and the number of Unique Molecular Identifiers (UMIs) (i. e. bona fide unique ligation events) that correspond to each position (examples of read-counts plotted against UMIs are shown in Additional file 7: Figure  S3 ). With the Rp6 oligo used in this study, the maximum UMI-count for a given position is 2187 (=37). This value was not reached in the data presented here, however if saturation becomes a problem in future projects, then the EMOTE-conv software permits down-sampling of the Illumina fastq-file, to get below saturation level.
The data in the EMOTE tables were then analysed as described in the results section to identify the TSSs. The reference sequences used were: NC_003923 (S. A number of statistical tools have been developed for mRNA-seq differential expression analysis  [52, 53] , however they are designed to compare mRNA levels of two separate RNA samples (each sample representing multiple biological replicates). In contrast, in TSS-EMOTE data, the comparison is within the same RNA sample, only treated with slightly different enzyme mixtures. Therefore, we favour a simple statistical test that rely on a betabinomial distribution to integrate this information, and is able to assign a p-value to each potential TSS: Let N + and N -be the number of reads that align onto the genome in the + RppH and the -RppH pools.
Let also q + and q -be the UMIs obtained for a given position on the genome in the two pools. Our model assumes that in absence of TSS, q + follows a beta-binomial distribution parameterized by q -, N -and N + as follows, P q þ N þ ; q -; N - j ð Þ¼β binomial β ¼ 1 þ N --q - a ¼ 1 þ q - q þ ; N þ ð Þ : The area under the upper tail of the distribution reflects how the data deviate from the model, and reveal our confidence in the position to be a TSS. Once the model for each -RppH/+RppH pair has been calculated, p-values of biological replicates of the TSS-EMOTE assay are further combined with Fisher's method. Probabilities are adjusted for multiple testing by computing the False Discovery Rate (FDR), and requiring TSS sites to have a FDR < 0. 01. Only candidates with q + ≥ 5 reads in one of the replicates are considered in this TSS detection process.
An example of the UMI-corrected read-counts mapping to the sarA locus of S. aureus are shown in Additional file 8: Figure  S4B , together with the TSSs previously identified by others (Additional file 1: Table  S1 ) and the TSSs identified in this study (Additional file 3: Table  S2 ). All computations are performed with the R programming language, making use of VGAM package for the beta binomial distribution  [54, 55] , and the R-scripts used are available upon request.
TSS annotation Identified TSSs further enter an annotation process where we determine: 1) if the TSS fall inside an annotated ORF; 2) the name of the first annotated ORF following the TSSs position in a strand specific manner; 3) the 50 bp surrounding sequence (between positions -45 and +5 around the TSS), into which we look for a match of the sigma-factor A recognition pattern TATAAT and TTGACA around positions -11 and -37 respectively. Additionally, TSSs that are less than 5 bp apart from each other are clustered into a TSS cluster, and the TSS with the smallest p-value is considered as the representative TSS (rTSS) of this cluster. Finally, S. aureus TSSs are annotated with the closest predicted non-coding RNA for MW2 strain taken from the staphylococcal regulatory RNA database  [22]. We also make use of the work of  [4]  on the sigma-factor B regulon to flag rTSSs that immediately precede an ORF upregulated by sigma-factor B in S.
Quality of cDNA was assessed using two pairs of primers 700 bp apart and located at the 5′ and 3′-end of the reference gene ELONGATION FACTOR-1α (EF-1α). Samples which threshold cycle (Ct) difference between the 5′ and 3′-end exceeded three cycles were not used further in the analysis. Presence of genomic DNA contamination was assessed by designing primers for PCR either on the junction of two exons to amplify only cDNA or on separated exons to amplify longer fragments on genomic DNA (Additional file 2: Table  S1 ). Gene sequences used for the primer design were retrieved from the Genome Database for Rosaceae (GDR, http://www. rosaceae. org/) Malus x domestica Whole Genome v1. 0 Assembly & Annotation. For the qRT-PCR reactions, 2 μl of the cDNA samples (diluted 1:20) was used as template in a 6-μl final reaction volume containing 3 μl of 2X Sybr green® (Roche) and 3 μM of each primer.
The PCR efficiencies were determined with a dilution range of a pool of all cDNA samples that was composed of seven data points (from 1:10 to 1:500). PCRs were run with two reference genes that are known to be stably expressed under the range of experimental conditions tested and have been previously used in apple: HistoneH3 and α-ACTIN  [22, 36, 51, 79]. Transcript levels were calculated with the LightCycler® 480 software (Version 1. 5. 0. 39, Roche). Statistical analyses of qRT-PCR results were performed using R  [78]. Normalized transcript levels were analyzed by ANOVA type III, considering the treatment ('ON' v. 'OFF'), date, tree, and bud nested in tree effects. The Student's t-test was performed to estimate the significance of the difference between 'ON' and 'OFF' treatments at each date.
Gene filtering and functional categorization Identification of transcripts that were significantly differentially expressed was performed using R  [78]  as detailed in the procedure R available on GitHub (https://github. com/Baptiste-Guitton/Microarray_bien-nial_bearing_apple). Only anti-sense probes, that hybridize sense transcripts, were considered in the analysis. Transcripts with log difference higher than 1 or lower than -1 associated with a significant p-value (<0. 01) and probe specificity lower or equal to 2 were selected for further analysis. Genes were then grouped depending on their expression profile. At each sampling date, each gene was tagged as "a" if its log 2 ratio was less than -1, "b" if its log 2 ratio was more than 1 or "c" if it exhibited no differential expression. The 3 3 possible combinations of a, b and c (minus 1 because the genes not differentially expressed at the three dates were not considered) constituted 26 possible groups of genes.
Construction of Venn diagrams was performed using the R function "draw. triple. venn" (library VennDiagram). Functional categorization analyses were performed using Arabidopsis thaliana genes homologous to apple transcripts present in the microarray, based on BLAST analysis against the TAIR database (https://www. arabidopsis. org/Blast/). Functional classification of genes was made using the web tool agriGO  [25]. First, Gene ontology (GO) was investigated for the whole subset of genes and then detailed date by date. Secondly, to identify enriched GO terms in the lists of differentially expressed genes at specific dates of the microarray analysis, singular enrichment analyses (SEA) were performed  [25]. Searches for protein interactions within gene sets were undertaken using STRING v9. 1  [30].
Genomic position of genes and QTL The genomic position of genes that showed significant differential expression for at least one of the three harvest dates was retrieved using the best hit reports of blastp of the Malus x domestica genome v1. 0 proteins file (Malus_x_domestica. v1. 0_gene_pep_-function_101210. formated. xls) made available by the Genome Database for Rosaceae (GDR, http://www. rosaceae. org/). Previous studies based on a QTL detection approach in an apple segregating population, 'Starkrimson' x 'Granny Smith' (STK x GS), identified eight genomic regions involved in control of biennial bearing and inflorescence yield  [24, 33]. Positions of SSR markers used to detect QTL were retrieved either by blasting the marker sequence (primer or amplicon) available on the Hidras database (Hidras, November 2014, http://www.
hidras. unimi. it/index. php) to the apple reference genome  [106] , or by using the Genome Database for Rosaceae (GDR, http://www. rosaceae. org/, file Malus_x_domestica. v1. 0. markers. xls). For SNP markers, the position of the gene prediction (MDP) used to design the marker  [33]  was considered as the genomic position of the marker (Additional file 2: Table  S2 ). QTL were defined between the two genetic markers flanking the QTL on the STK x GS integrated genetic map (Additional file 2: Table  S3 ). Since the chr10 QTL was detected at the very bottom of the linkage group and flanking markers exhibited inverted order between the genetic and the physical maps, in our analyses we considered the lower part of chr10, below the MS06g03 genetic marker, as the QTL confidence interval.
Screening for expression of flowering genes by qRT-PCR and in the microarray The expression profile of transcript levels of key flowering genes previously published in various apple cultivars were studied by qRT-PCR over five time points. This enabled us to compare our results with the literature, interpret the profiles based on the known function of these genes, and to choose three out of five time points available for microarray analyses. Specific and unique PCR-products were amplified for MdFT1, MdFT2, AFL1, AFL2, MdAP1a, MdAP1b, MdSOC1-like and MdTFL1, which was confirmed by melting curve analyses and by sequencing of PCR amplicons (data not shown). Transcript levels were normalized against the mRNA expression level of the reference gene HistoneH3 of apple, which enabled us to compare our results with previous studies  [51]  (Fig.
At day 131, the genes with sequence homology to response to stimulus GO including the response to abiotic stress, the response to stress and the cellular response to stimulus GO were significantly up-regulated (Additional file 5: Figure  S4A , Additional file 2: Table  S7A ). At day 151, four main biological processes were differentially expressed: (i) the response to stimulus, with the response to stress GO being the most significant of the dataset (p-value = 8. 90E-11) with, among others, the response to oxidative (See figure on previous page. ) Fig. 2  Expression pattern of MdFT1, MdFT2, AFL1, AFL2, MdAP1a, MdAP1b, MdSOC1-like and MdTFL1 during the growing season in 'Gala' apple trees measured by quantitative real-time PCR using spur apical buds harvested at day 118, 131, 151, 180 and 222.
Selection of differentially expressed transcripts in apple spur apical buds between 'ON' and 'OFF' trees. The gene identifier is reported along with the Arabidopsis gene homolog, a short annotation, BLAST results against TAIR database (percentage of identity and E-value), position on the apple genome (chromosome and position in bp), and the pattern of expression at day 131, 151 and 222. Negative log 2 ratios correspond to transcripts up-regulated in 'ON' trees.
Acknowledgements We thank the genotyping platform of  CIRAD (Montpellier, France ) for providing the facilities for RNA extraction and qRT-PCR measurements and the  ANAN platform of the QuaSaV research federation  for providing the microarray facility. We acknowledge  T. Legavre  for fruitful discussions and valuable advice on RNA extractions, as well as  S. Gardiner  and  S. Tustin  for critical reading of the manuscript. This work was supported by the  Plant Breeding Department of the National Institute of Agronomic Research of France (INRA) , by The  New Zealand Institute for Plant & Food Research Limited  ( Pipfruit Internal Investment Project ), and by the  New Zealand Ministry of Business Innovation and Employment [Horticultural Genomics programme  ( CO6X0810 )].
Availability of data and materials Microarray samples were deposited in NCBI's Gene Expression Omnibus  [5]  and are accessible through GEO Series accession number GSE64646 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?token=onsdgqwotbuhbox&acc =GSE64646). Normalized data along with the R script used for analyzing the data were deposited on GitHub (https://github. com/Baptiste-Guitton/Microarray_biennial _bearing_apple_Gala) that enable the reproducibility of the analyses.
S. , mainly affected by HCV infection and clinically followed for up to 23 years  (Hoshida et al. , 2008; Hoshida et al. , 2013; King et al. , 2015). The gene signature successfully monitored the HCC chemopreventive effect of an FDA-approved small molecule epidermal growth factor (EGF) pathway inhibitor, erlotinib, in multiple rodent models of cirrhosis-driven HCC  (Fuchs et al. , 2014) , which led to initiation of a proof-of-concept, biomarker-guided cancer chemoprevention clinical trial (ClinicalTrials. gov, NCT02273362). However, it is still undetermined whether the gene signature is universally applicable beyond HCV. Moreover, adverse effects of erlotinib limit its wide-spread use as a preventive medicine and therefore indicate the necessity to further identify better HCC chemoprevention targets.
Pan-etiology HCC risk gene signature In our previous studies, the HCC risk gene signature has been validated for its association with future cancer risk in fibrotic/cirrhotic patients mainly affected by chronic HCV infection (Figure  S1A , Cohort 1-4, n=668 in total)  (Hoshida et al. , 2008; Hoshida et al. , 2013; King et al. , 2015). Reanalysis of publicly available datasets of patient series affected with HBV, alcohol abuse, and NASH showed significant association of the signature with clinical outcome or disease severity (Figure  S1B ). To verify the findings with a clinically applicable assay, we implemented a reduced version of the 186-gene HCC risk gene signature bioinformatically defined in our recent study (32-gene signature)  (King et al. , 2015)  in an FDA-approved digital transcript counting technology (Elements assay, NanoString).
To identify functional regulators of the genes in module no. 8, we systematically surveyed enrichment of experimental genetic perturbation transcriptome signatures defined by shRNA library-based knockdown of 5,272 genes in an unbiased manner (the transcriptome signatures were generated using the dataset from NIH Library of Integrated Cellular Signatures [LINCS] project, www. lincsproject. org) (see Supplemental Experimental Procedures). Top enriched upstream regulator genes were AKT1, SLC35A1, DDX42, LPAR1, and ILK (Figure  4B ). Indeed, Akt/mechanistic target of rapamycin (mTOR)/ phosphoinositide 3-kinase (PI3K) pathway inhibitors, everolimus and sirolimus, are currently being tested in clinical trials for prevention of post-liver transplantation HCC recurrence  (Burra and Rodriguez-Castro, 2015).
, 2007). In consistent, AKAP13, an HCC risk signature member gene, encodes a guanine exchange factor that relays the signal to downstream of the RhoA signaling pathway, further supporting the role of LPA pathway as a functional driver of HCC development. It is known that RhoA, Ras/mitogen-activated protein kinase kinase (MAPK)/extracellular signal-regulated kinase (ERK)/MAPK/ERK kinase (MEK), Akt/PI3K, and phospholipase C (PLC) pathways are downstream effectors of the LPA pathway  (Kihara et al. , 2015). In Cohort 5, expression of NCAPH (RhoA pathway target), PRKG2 (K-Ras pathway target), SERPINB2 (H-Ras pathway target), and GPX2 (MEK pathway target) gradually increased according to the HCC risk signature-based stratification (Figure  S3D ). Transcriptome-based unbiased in silico screening of 20,413 chemical perturbation in the LINCS database using the HCC risk gene signature as a query yielded selective inhibitors of Rho kinase, Akt/PI3K, and MAPK/ERK/MEK pathways, further supporting the involvement of the LPA downstream pathways (Table  S7 ).
HCC risk gene signature profiling Archived FFPE liver tissues were analyzed by the 32-gene HCC risk signature  (King et al. , 2015)  implemented in nCounter Elements assay (NanoString). Prognostic prediction was performed as previously described  (King et al. , 2015). Prognostic association of the gene signature was evaluated by log-rank test and multivariable Cox regression modeling adjusted for clinically well-established prognostic variables. All analyses were performed using R statistical language (www. r-project. org) and GenePattern (www. broadinstitute. org/ genepattern).
, 2014). Gene expression was measured by real-time PCR. Total/phosphorylated MRLC, collagen 1, and α-SMA protein levels were determined by western blotting as previously described  (Fuchs et al. , 2014). Liver fibrosis stained by sirius red was quantified using Image J software (imagej. nih. gov/ij) as collagen proportionate area. Intergroup difference was evaluated by t-test with Bonferroni correction as needed.
Transcriptome profiling of rodent livers Transcriptome profiling of rodent liver was performed by RNA-Seq, RatRef-12 beadarray, or MouseRef-8 beadarray (Illumina) as previously described  (Fuchs et al. , 2014). Rodent genes were converted into orthologous human genes using a mapping table obtained from NCBI HomoloGene database (build 68). Induction or suppression of target genes of relevant cellular signaling pathways was assessed by GSEA. The HCC risk gene signature Elements assay probes are available upon request to the lead contact (Y. H. ) or at www. hoshida-lab. org.
See also Figure  S2  and Tables  S2, S3 , S4, and S5. In "Cirrhosis (human)" column, genes in the transcriptome dataset were rank-ordered according to differential expression between cirrhotic and healthy livers to compute GSEI. In "HCC high risk (human)" column, genes were rank-ordered according to association with time to HCC development measured by Cox score  (Hoshida et al. , 2008)   See also Figure  S3  and Table  S7.
S. N. was supported by Uehara Memorial Foundation. N. G. was supported by  FLAGS foundation, Nuovo-Soldati Cancer Research Foundation , and an advanced training grant from  Geneva University Hospital. M. Y. , W. C. , and C. D. were supported partially by  Kaohsiung Medical University "Aim  for the  Top Universities Grant , grant No. KMU-TP103E06  & No. KMU-TP103E07  & No. KMU-TP103E08 ", and by a grant from  Kaohsiung Medical University Hospital  ( KMUH102-2T01 ). T. F. B. was supported by  ARC ,  Paris, Institut Hospitalo-Universitaire, Strasbourg  ( IHUARC IHU201301187 ),  Laboratory of Excellence HEPSYS  ( ANR-10-LABX-0028 ), and  European Union  ( ERC-2014-AdG-671231-HEPCIR ). R. T. C. was supported by  NIH/NIDDK  ( DK078772 ,  DK098079 ). B. C. F. was supported by  NIH/NCI  ( CA140861 ),  NIH/NIDDK  ( DK104956 ), and grant from  American Institute for Cancer Research.
This paper explores the characteristics of 42 solar X-class flares that were observed between February 2011 and November 2014, with data from the Solar Dynamics Observatory (SDO) and other sources. This flare list includes nine X-class flares that had no associated CMEs. In particular our aim was to determine whether a clear signature could be identified to differentiate powerful flares that have coronal mass ejections (CMEs) from those that do not. Part of the motivation for this study is the characterization of the solar paradigm for flare/CME occurrence as a possible guide to the stellar observations; hence we emphasize spectroscopic signatures. To do this we ask the following questions: Do all eruptive flares have long durations? Do CME-related flares stand out in terms of active-region size vs.