dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic analysis Phylogenetic relationships were inferred using the VP60 gene sequences of seven RHDV2 characterized in brown hares and rabbit lagovirus VP60 sequences available in databases. We also included the VP60 gene sequences of nine French RHDV2 circulating in wild rabbits and farms between January 2013 and November 2014 (Additional file 1). The sequence of the French reference EBHSV strain EBHSV-GD (Z69620) was used as an outgroup to root the trees. Phylogenetic analyses were conducted in MEGA software version 5  [36]. The neighbor-joining method was implemented with the pairwise deletion option and based on the Kimura 2-parameter model including transition and transversion substitutions. Codon positions included were 1st + 2nd + 3rd. Search for the trees was implemented with the closeneighbor-interchange algorithm.
Nucleotide VP60 sequence accession numbers Nucleotide VP60 sequences of six RHDV2 characterized in French hares (E13-37, E14-03, E14-40, E14-45, E14-115 and E15-12) and nine RHDV2 characterized in French rabbits (13-22, 13-69, 13-71, 13-100, 13-122, 13-165, 14-02, 14-59 and 14-114) are available in databases under the EMBL/GenBank Accession Numbers LT168833 to LT168847. The RHDV2 E14-73 VP60 sequence is available in databases under the Accession Number LT549473.
Acknowledgements We thank the departmental public veterinary laboratories which performed EBHS diagnostic and sent us liver samples to characterize the viruses responsible for the observed mortalities: " Inovalys-Angers" (Angers, France)  and  "Laboratoire de l'Environnement et de l' Alimentation de la Vendée" (La Roche -sur-Yon, France). We thank the departmental services of the ONCFS, the  French departmental , regional and national hunter federations, the departmental public veterinary laboratories, the  Anses Laboratory for Rabies and Wildlife of Nancy (LRFSN)  and all the people who reported wildlife mortality events. We also thank  Julie Tucoulet  ( ONCFS ) for her assistance with SAGIR data. We are grateful to  Jérémy Dufay  and  Marina Chatel  who contributed to developing the real-time RT-PCR EBHSV and to screening the lagoviruses for the molecular epidemiological survey, respectively.
Funding This work was performed within the framework of the  ECALEP  project supported by the  ERA-Net Anihwa (Animal Health and Welfare) , a coordination action funded under the  European Commission 's  ERA-Net scheme within the Seventh Framework Programme  (Contract No. 291815 ). The  ECALEP  project is funded by the  ANR  ( France , contracts  ANR-14-ANWA-0004-01 ,  ANR-14-ANWA-0004-02 ,  ANR-14-ANWA-0004-03 ,  ANR-14-ANWA-0004-04 ), the  Ministry of Health, Dep. for Veterinary Public Health, Nutrition & Food Safety (Italy)  and the  Research council FORMAS (Sweden , contract  FORMAS 221-2014-1841 ). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Design and patients All patients in the study centres who underwent electrical or pharmacological cardioversion of AF or flutter were included in the registry. Patient records were collected in seven cardiology departments of six different European cities in five European countries, namely (i) Budapest-Hungary, (ii) Budapest-Hungary, (iii) Pisa-Italy, (iv) Bari-Italy, (v) Madrid-Spain, (vi) Amiens-France, and (vii) Kaunas-Lithuania. Data were recorded between September 2014 and October 2015, in the 7 months prior to start date of the registry, and in the 6 months following the start date of the registry. The retrospective and prospective design was chosen in order to align the timing to the 12 months following the presentation of the X-VERT trial, in order to assess whether its publication would influence NOAC uptake in the cardioversion setting.
Statistical methods We used the Pearson chi-squared test to compare NOAC vs. VKA in de novo prescriptions prior to scheduled cardioversion (yes/no), duration of OAC before cardioversion (more/less than 3 weeks), and duration of OAC after cardioversion (more/less than 4 weeks). The Pearson chisquared test was also used to compare Spain and France in use of anticoagulants (yes/no). To analyse changes in OAC usage over time, we used logistic regression with OAC (yes/no) as the outcome variable and the number of months since 1 January 2014 as explanatory variable. The plots of OAC usage over time were smoothed with 1-nearest-neighbour and 2-nearest-neighbour averaging. Stata 14 (StataCorp LP) and Matlab 2014 (Mathworks, Inc) were used to perform the statistical analyses.
In a Danish registry between 2011 and 2015, a decreasing usage of VKA in general AF management was observed, and among  NOACs a rise of apixaban and a decrease of dabigatran was noticed. 23 Similarly, an expanding use of NOACs was reported in the GARFIELD-AF registry, and there were differences between countries similar to our results in the cardioversion population. 24 ifferences of NOAC usage between countries depend predominantly on regulatory as well as economic factors. The design of this registry purposely focused on the use of oral anticoagulants, however outcome data of stroke and systemic embolism and safety endpoints such as bleeding events would be of great additional value to this work. These will be subject for future research and will receive highest priority.
Quantitative RT-PCR Total RNA from mouse cells or tissues was extracted with TRIzol reagent (Takara) and 500 ng of RNA were used to generate cDNA using a Reverse Transcription Kit (Takara). Gene expression analysis was done using the SYBR Green PCR Master Mix (Takara) according to the manufacturer's instructions and data were normalized by the level of Gapdh or actin expression in each individual sample. The sequences of the primers used in this study for quantitative real-time RT-PCR are listed in Table  1.
In the EPIC Norfolk study, however, 61% (n=15 000) of the participants were excluded from the analysis because of missing covariates, possibly giving a selective picture of the association between CVH and outcomes. To the best of our knowledge, this is the first study addressing the possible heterogeneity in the association of CVH status with incident CHD and stroke subtypes. ] [9] [10] [11] [12]  For instance, in PRIME, we previously showed differential associations of lipids with incident CHD as compared with stroke over 10 years, and heterogeneous associations of traditional risk factors with incident stable angina as compared with acute coronary syndrome. 9, 10 More recently, data from the CALIBER (Cardiovascular disease research using linked bespoke studies and electronic health records) study based on nearly 2 million participants from primary care practices in England reported a highly heterogeneous association of smoking status with lifetime risk for 12 first manifestations of CVD.
Acknowledgments We thank the following organizations that allowed the recruitment of the PRIME subjects: the Health screening centers organized by the  Social Security of Lille (Institut Pasteur), Strasbourg, Toulouse , and Tourcoing;  Occupational Medicine Services of Haute-Garonne , of the  Urban Community of Strasbourg ; the  Association Inter-entreprises des Services M edicaux du Travail de Lille  et environs; the  Comit e pour le D eveloppement de la M edecine du Travail ; the  Mutuelle G en erale des PTT du Bas-Rhin ; the  Laboratoire d'Analyses de l'Institut de Chimie Biologique de la Facult e de M edecine de Strasbourg ; the  Department of Health and Social Services and Personal Safety (NI) , its  Research and Development Office , and the  Northern Ireland Chest Heart and Stroke Association.
Analysis of the impact of AR variants on potential N-cadherin regulators We have previously shown a difference in the capacity of full-length AR (AR-FL) and AR variants to upregulate N-cadherin expression in PCa cells  [24]. We investigated whether AR variants could directly deregulate known N-cadherin transcriptional regulators. We performed an RNA-seq experiment in LNCaP cells overexpressing AR-WT or AR-V7. From this analysis, we identified 751 upregulated genes and 108 downregulated genes in LNCaP cells overexpressing AR-V7 compared to AR-WT (Filter criteria: log2Fold-Change > 1 and an adjusted p-value for multiple testing < 0. 05). First, biological functions of deregulated genes were studied using IPA software. Interestingly, deregulated genes in the presence of AR-V7 in LNCaP cells were associated with functions such as cellular movement, cell morphology or cellular assembly and organization (Supplementary Figure  1 ).
As expected, CDH2 was found in the list of upregulated genes involved in these functions. In the next step, we searched, using two different methods, the differentially expressed genes that encode for transcription factors able to regulate N-cadherin expression. First, we identified differentially expressed transcription factors in the presence of AR-V7 with predicted binding sites in CDH2 gene using SABiosciences proprietary database DECODE (DECipherment Of DNA Elements) (Figure  1A ). Among these factors, only SOX9 has been experimentally linked to N-cadherin expression  [33]. We further looked to confirm the increased expression of SOX9 in the presence of AR-V7 compared to AR-WT in our LNCaP model. We used a doxycycline inducible system for the expression of AR variants in LNCaP cells followed by qRT-PCR and Western Blot analyses.
Unfortunately, we did not observe any increase in SOX9 expression in the presence of AR-V7 (data not shown). We continued the RNA-seq analysis using the IPA software and identified a network in which ETV5 was a direct regulator of N-cadherin in our model (Figure  1B ). The increase of ETV5 mRNA levels in the presence of AR-V7 was confirmed with the doxycycline inducible AR-V7 expression system (Supplementary Figure  2 , Figure  1C ). Nevertheless, ETV5 protein was not detected in immunoblots from LNCaP cells overexpressing AR variants (Supplementary Figure  3 ). Finally, we investigated whether ETV5 silencing could decrease N-cadherin upregulation in the presence of AR variants. We used the doxycycline inducible AR-V7 expression system and ETV5 specific siRNAs (Figure  1D , left panel) and could not observe any change in N-cadherin expression in the presence of AR-V7 (Figure  1D , right panel).
To go further in our analysis, we performed an integrative analysis between the RNA-seq and miRNAseq using "micro RNA target filter" in IPA software to search for potential targets of microRNAs and we obtained a list of mRNA-microRNA pairs. Because microRNAs negatively regulate the expression of mRNA, we have selected only pairs with an anticorrelated expression pattern. Finally, we have obtained 75 potential pairs for upregulated microRNAs and 279 pairs for downregulated microRNAs (Figure  2 ). In particular, the miRNA-seq analysis revealed that miR-221-3p and miR-26b-5p could regulate N-cadherin expression. However, according to our miRNA-seq data, the expression of miR-221-3p and miR-26b-5p is only decreased by a 1. 2-fold and 1. 1-fold respectively in the presence of AR-V7 compared to AR-WT.
610920, BD Biosciences), b-actin (1:2000, sc-47778, Santa Cruz Biotechnology) or GAPDH (1:1000, sc-20357, Santa Cruz Biotechnology) at 4°C overnight. Blots were washed and incubated with horseradish peroxidase-conjugated goat anti-mouse (1:2000, sc-2005, Santa Cruz Biotechnology), goat antirabbit (1:5000, sc-2004, Santa Cruz Biotechnology), rabbit anti-goat (1:2000, sc-2768, Santa Cruz Biotechnology) or rat anti-mouse IgG2a (1:1000, catalog no. 553391, BD Biosciences) secondary antibodies for 1 h. Immunoreactive proteins were visualized by chemiluminescence (Immobilon TM Western, Millipore). For Stain-Free gels, total protein normalization was performed using Image Lab TM Software (Bio-Rad Laboratories).
For statistical analysis, comparison of samples was performed using the method proposed by Love et al. [54]  implemented in the DESeq2 Bioconductor library (DESeq2 v1. 0. 19). Resulting p-values were adjusted for multiple testing by using the Benjamini and Hochberg (1995) method  [55]  (GEO dataset GSE71334). Functional and pathway analysis of differentially expressed genes in the presence of AR variants were performed using Ingenuity ® Pathway Analysis tool (IPA, www. ingenuity. com, Qiagen).
miRNA-seq analysis RNA samples used for RNA-seq were also analyzed by miRNA-seq. As mentioned above, the library of cDNA and the sequencing were performed by IGBMC Microarray and Sequencing platform. For data analysis, adapters were trimmed from total reads using FASTX_Toolkit (http://hannonlab. cshl. edu/ fastx_toolkit/). Only trimmed reads with a length between 15 and 40 nucleotides were kept for the further analysis. Data analysis was performed according to published pipeline ncPRO-seq  [56]. Briefly, reads were mapped onto the Hg19 genome assembly with Bowtie v0. 12. 8  [57]. The annotations were done with miRBase release 20 for the microRNAs, with Repbase for the repeats and with Rfam for the other small non-coding RNAs. The normalization and differential expression analysis were done with DESeq2 R package v1.
Integrative analysis Integrative analysis between RNA-seq and miRNAseq was performed by comparing a list of differentially expressed genes (DEG) and differentially expressed miRNA (DEM). The list of DEG comprised all the genes with a fold change ≤ or ≥ 1. 5 and adjusted p-value ≤ 0. 05 and for the list of DEM, we have selected all the miRNA differentially expressed with adjusted p-value ≤ 0. 05. These two lists were separately uploaded in Ingenuity Pathway Analysis (IPA) software (www. ingenuity. com, Qiagen) and the putative targets of DEM were identified using microRNA Target Filter. Briefly, this tool is the combination of four databases based on prediction binding (TargetScan) and experimentally validated interaction (miRecords, Tarbase and Ingenuity ® Knowledge base). For further analyses, only pairs of mRNA-miRNA with anticorrelated expression were selected.
ACKNOWLEDGMENTS Sequencing was performed by the  IGBMC Microarray and Sequencing platform , member of the  France Genomique program. We would like to thank  Irwin Davidson  and  Igor Martianov  at the IGBMC for their support for ChIP experiments. Also, we would like to thank  Dr. Kevin White  at  Institute for Genomics and Systems Biology, University of Chicago  for EGFP antibody providing.
Determination of metabolite concentrations The ethanol, glycerol, succinate and acetate concentrations were determined by HPLC (HPLC 1290 Infinity, Agilent™ Technologies, Santa Clara, California, USA) on a Phenomenex Rezex ROA column (Phenomenex™, Le Pecq, France) at 60 °C. The column was eluted with 0. 005 N H2SO4 at a flow rate of 0. 6 mL/min. The acetic acid concentration was determined with a UV meter at 210 nm; the concentrations of the other compounds were determined with a refractive index detector. The analysis was performed with an Agilent™ EZChrom software package.
Filter papers were imaged using UV light and analyzed using Image J Software using the threshold technique in double-blind manner. Image J particle analysis was performed on spots greater than 6 mm 2 (corresponding to 0. 6 μl urine), reducing non-specific marks potentially deposited by paws and tails that pass through urine spots. Ledge assay. In the ledge test, mice were placed on the elevated cage's ledge at a height of 35 cm and 0. 8 cm wide, and monitored their movement. Each mouse was tested three times (each test takes 20 s) and scored from 0 to 3 depending on the severity of deficits in a double-blind manner. Scoring is as follows: if the mouse walked along the ledge, without foot faults (i. e. , loosing footing) and back into the cage delicately, score of 0; if the mouse demonstrated any foot fault while walking on the ledge, score of 1; if the mouse did not effectively walk on or dismounted the ledge immediately, score of 2; if the mouse fell off the ledge or avoided walking, score of 3.
For probe trials, mice were placed in the tank with the platform removed and given 60 s to explore the tank. Noldus Ethovision 9 software tracked swim speed, total distance moved, and time spent in the target quadrant where the platform was previously located. Novel location recognition test. The Novel object recognition test consisted of an open field-box (44 × 44 cm). The habituation period was 5 min daily of free exploration in the arena containing two different objects (15 mm diameter) over 3 days. On test day, the animals were allowed to explore three identical objects (with same color Lego sets) placed into the area in fixed location for 6 min and the time spent inspecting the individual objects was recorded (Noldus Ethovision XT). Without any time-interval the animals were replaced into the box where one object was placed into a new location the mice were allowed to explore them for an additional 3 min.
Acknowledgements We thank  Dr Michael Whalen  for his expert advice and comments on the manuscript. O. A. is an Alzheimer's  Association Research Fellow. The work is supported by grants from  NIH  ( T32 HD40128-11A1 ) to R. M. ,  NIH  ( R01AG029385 ,  R01AG046319 ,  R01CA167677 ) Alzheimer's  Association  ( DVT-14-322623 ),  Alzheimer's Association, Alzheimer's Research UK  and  Weston Brain Institute  ( MCDN-15-368711 ),  Thome Memorial Foundation in Alzheimer's Disease Drug Discovery Research  to K. P. L, by  NFLPA  pilot grants to K. P. L. , X. Z. Z, and  R. M. , and  BIDMC  pilot grant and gift donations from the  Owens Family Foundation  to X. Z. Z. and K. P. L.
However, standard measurements are yet to be defined since numerous software exist for RNA-seq processing, including many different gene expression normalization methods  12. Such transcriptomic investigations generated a large amount of data. Consequently, databases were set up to standardize all information associated with expression matrices, notably: organism, platform identifier, normalization, unit measurement and study-specific information (i. e. cell types, treatments, time series, sampling and culture conditions, etc. ). Among the many available gene expression databases, the two most used are Gene Expression Omnibus from the National Center for Biotechnology Information (http://www. ncbi. nlm. nih. gov/geo)  13  and ArrayExpress from the European Bioinformatics Institute (http://www.
These genes were identified based on differential expression analyses with three different classifiers: FNCLCC grade, genomic alteration number and chromosomal instability signature  19. Gene ontologies associated with these genes are mitosis and chromosome integrity pathways. This signature, called CINSARC (Complexity INdex in SARComas), was also informative in diffuse large B-cell lymphomas and breast carcinomas. We extended its application scope to sarcomas with simple genetics (harboring recurrent and specific genomic alteration): gastrointestinal stromal tumors and synovial sarcomas  26, 27. Venet et al. demonstrated that randomly generated gene sets could be better predictors compared to published signatures in breast cancers  28. Based on this observation, we compared the prognostic value of CINSARC to 1000 equal-sized randomly generated gene sets on four independent sarcoma datasets.
We reported that no randomly generated signature was a better predictor than CINSARC  29. Recently, Gentles et al. reported an extensive data-mining analysis and compiled their results into a new resource named PRECOG (PREdiction of Clinical Outcomes from Genomic profiles; https://precog. stanford. edu)  30. They evaluated the prognostic value of 23,287 genes across 39 cancer types from 166 published expression datasets, approximately representing 18,000 tumors. Experiments were performed on genes associated with good outcome, particularly genes involved in the immune system with a particular interest for the CIBERSORT (Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts) approach  31. Gentles et al. also observed that prognostic genes are significantly more shared by different tumor types than expected by chance, either associated with good or poor outcome.
Results Signature enrichments at pan-cancer level. To obtain an exhaustive set of gene expression signatures, we used the MSigDB (Molecular Signatures DataBase; v5. 2), including 18,026 gene sets, and the GeneSigDB (Gene Signature DataBase; v4. 0), including 2,951 human gene sets  32, 33. CINSARC has already been included in GeneSigDB (named SARCOMA CHIBON10 67GENES GENOMECOMPLEXITYPREDICTOR), but lacks several genes (62 instead of 67). Consequently, we included the full signature named SARCOMA CHIBON10 67GENES GENOMECOMPLEXITYPREDICTOR CURATED. We performed the popular Gene Set Enrichment Analysis (GSEA) method to determine signature enrichments in genes significantly associated to poor outcome (see methods)  34. As recommended by GSEA documentation, we removed signatures with less than 25 known genes in PRECOG database to avoid inflated scorings for very small gene sets.
This filtered out 4,394 (24%) and 1,085 (37%) signatures from MSigDB and GeneSigDB, respectively. Of note, BORA (formerly known as C13orf34) was not evaluated in PRECOG and is therefore missing in both full (curated) and reduced (62 genes) CINSARC. In the database of 15,499 gene sets, 1,273 (8%) were significantly enriched in genes associated with poor prognosis (family-wise error rate P < 0. 05; Supplementary Table  1 : sheet 1). Sorted by decreasing normalized enrichment scores (NES; primary statistic for examining gene set enrichment results; see methods), reduced and full CINSARC ranked 9 and 17, respectively (Figs  1  and 2 ). In addition to a high NES, leading-edges metrics (extension of the NES results; see methods) showed high sensitivity (tags are 97% and 95%, ranked 4 and 11 for reduced and full CINSARC, respectively), low false-negative rate (lists are 2% and 3%, ranked 5 and 25 for reduced and full CINSARC, respectively) and high enrichment signal strength (signals are 99% and 98%, ranked 11 and 15 for reduced and full CINSARC, respectively).
This can be explained by the fact that collective information of each individual gene prognostic value may be different from the prognostic value given by a whole set of genes. Accordingly, we investigated whether CINSARC was associated with survival differences in studies used to build the PRECOG resource. To obtain a robust evaluation, we filtered out datasets with less than 50 annotated cases, less than 25 CINSARC genes and <10% or >90% death rates. Accordingly, 83 datasets (out of 166; 50%) were investigated using the Kaplan-Meier estimator, covering 27 different cancer types. Since the investigated datasets did not permit generation of training-validation cohorts (median number of samples was 111), we performed leave-one-out cross-validation as the classifier method. A total of 33 datasets (40%) produced significant survival differences according to the CINSARC classification (Supplementary Table  2 , Supplementary Figures  2  and 3 ).
The 17 cancer types associated with these 33 datasets are: neuroblastoma, breast cancer, Mantle cell lymphoma, diffuse large B-cell lymphoma, ovarian cancer, prostate cancer, acute myeloid leukemia, lung adenocarcinoma, bladder cancer, liver cancer, multiple myeloma, follicular lymphoma, colon cancer, squamous cell carcinoma of the lung, oral squamous cell carcinoma, chronic lymphoid leukemia and B-cell acute lymphoblastic leukemia. Importantly, cancer types where CINSARC was significantly enriched in top-ranked genes demonstrated higher proportion of significant survival differences (22 among 42 datasets; 52%) compared to the others (11 among 41; 27%). Also, cancer types with at least 10 genes associated with poor prognosis demonstrated higher proportion of significant survival differences (25 among 56 datasets; 45%) compared to the others (8 among 27; 30%).