Outcomes Associated With Oral Anticoagulants Plus Antiplatelets in Patients With Newly Diagnosed Atrial Fibrillation.

Importance: Patients with nonvalvular atrial fibrillation at risk of stroke should receive oral anticoagulants (OAC). However, approximately 1 in 8 patients in the Global Anticoagulant Registry in the Field (GARFIELD-AF) registry are treated with antiplatelet (AP) drugs in addition to OAC, with or without documented vascular disease or other indications for AP therapy. Objective: To investigate baseline characteristics and outcomes of patients who were prescribed OAC plus AP therapy vs OAC alone. Design, Setting, and Participants: Prospective cohort study of the GARFIELD-AF registry, an international, multicenter, observational study of adults aged 18 years and older with recently diagnosed nonvalvular atrial fibrillation and at least 1 risk factor for stroke enrolled between March 2010 and August 2016. Data were extracted for analysis in October 2017 and analyzed from April 2018 to June 2019. Exposure: Participants received either OAC plus AP or OAC alone. Main Outcomes and Measures: Clinical outcomes were measured over 3 and 12 months. Outcomes were adjusted for 40 covariates, including baseline conditions and medications. Results: A total of 24 436 patients (13 438 [55.0%] male; median [interquartile range] age, 71 [64-78] years) were analyzed. Among eligible patients, those receiving OAC plus AP therapy had a greater prevalence of cardiovascular indications for AP, including acute coronary syndromes (22.0% vs 4.3%), coronary artery disease (39.1% vs 9.8%), and carotid occlusive disease (4.8% vs 2.0%). Over 1 year, patients treated with OAC plus AP had significantly higher incidence rates of stroke (adjusted hazard ratio [aHR], 1.49; 95% CI, 1.01-2.20) and any bleeding event (aHR, 1.41; 95% CI, 1.17-1.70) than those treated with OAC alone. These patients did not show evidence of reduced all-cause mortality (aHR, 1.22; 95% CI, 0.98-1.51). Risk of acute coronary syndrome was not reduced in patients taking OAC plus AP compared with OAC alone (aHR, 1.16; 95% CI, 0.70-1.94). Patients treated with OAC plus AP also had higher rates of all clinical outcomes than those treated with OAC alone over the short term (3 months). Conclusions and Relevance: This study challenges the practice of coprescribing OAC plus AP unless there is a clear indication for adding AP to OAC therapy in newly diagnosed atrial fibrillation

RNA-Seq improves annotation of protein-coding genes in the cucumber genome

<p>Abstract</p> <p>Background</p> <p>As more and more genomes are sequenced, genome annotation becomes increasingly important in bridging the gap between sequence and biology. Gene prediction, which is at the center of genome annotation, usually integrates various resources to compute consensus gene structures. However, many newly sequenced genomes have limited resources for gene predictions. In an effort to create high-quality gene models of the cucumber genome (<it>Cucumis sativus </it>var. <it>sativus</it>), based on the EVidenceModeler gene prediction pipeline, we incorporated the massively parallel complementary DNA sequencing (RNA-Seq) reads of 10 cucumber tissues into EVidenceModeler. We applied the new pipeline to the reassembled cucumber genome and included a comparison between our predicted protein-coding gene sets and a published set.</p> <p>Results</p> <p>The reassembled cucumber genome, annotated with RNA-Seq reads from 10 tissues, has 23, 248 identified protein-coding genes. Compared with the published prediction in 2009, approximately 8, 700 genes reveal structural modifications and 5, 285 genes only appear in the reassembled cucumber genome. All the related results, including genome sequence and annotations, are available at <url>http://cmb.bnu.edu.cn/Cucumis_sativus_v20/</url>.</p> <p>Conclusions</p> <p>We conclude that RNA-Seq greatly improves the accuracy of prediction of protein-coding genes in the reassembled cucumber genome. The comparison between the two gene sets also suggests that it is feasible to use RNA-Seq reads to annotate newly sequenced or less-studied genomes.</p

Improved annotation of 3' untranslated regions and complex loci by combination of strand-specific direct RNA sequencing, RNA-seq and ESTs

The reference annotations made for a genome sequence provide the framework for all subsequent analyses of the genome. Correct annotation is particularly important when interpreting the results of RNA-seq experiments where short sequence reads are mapped against the genome and assigned to genes according to the annotation. Inconsistencies in annotations between the reference and the experimental system can lead to incorrect interpretation of the effect on RNA expression of an experimental treatment or mutation in the system under study. Until recently, the genome-wide annotation of 3-prime untranslated regions received less attention than coding regions and the delineation of intron/exon boundaries. In this paper, data produced for samples in Human, Chicken and A. thaliana by the novel single-molecule, strand-specific, Direct RNA Sequencing technology from Helicos Biosciences which locates 3-prime polyadenylation sites to within +/- 2 nt, were combined with archival EST and RNA-Seq data. Nine examples are illustrated where this combination of data allowed: (1) gene and 3-prime UTR re-annotation (including extension of one 3-prime UTR by 5.9 kb); (2) disentangling of gene expression in complex regions; (3) clearer interpretation of small RNA expression and (4) identification of novel genes. While the specific examples displayed here may become obsolete as genome sequences and their annotations are refined, the principles laid out in this paper will be of general use both to those annotating genomes and those seeking to interpret existing publically available annotations in the context of their own experimental dataComment: 44 pages, 9 figure

Do international institutions matter? Socialization and international bureaucrats

A key component of (neo-)functionalist and constructivist approaches to the study of international organizations concerns staff socialization. Existing analyses of how, or indeed whether, staff develop more pro-internationalist attitudes over time draw predominantly on cross-sectional data. Yet, such data cannot address (self-)selection issues or capture the inherently temporal nature of attitude change. This article proposes an innovative approach to the study of international socialization using an explicitly longitudinal design. Analysing two waves of a large-scale survey conducted within the European Commission in 2008 and 2014, it examines the beliefs and values of the same individuals over time and exploits exogenous organizational changes to identify causal effects. Furthermore, the article theorizes and assesses specified scope conditions affecting socialization processes. Showing that international institutions do, in fact, influence value acquisition by individual bureaucrats, our results contest the widely held view that international organizations are not a socializing environment. Our analysis also demonstrates that age at entry and gender significantly affect the intensity of such value change

New Assembly, Reannotation and Analysis of the Entamoeba histolytica Genome Reveal New Genomic Features and Protein Content Information

Entamoeba histolytica is an anaerobic parasitic protozoan that causes amoebic dysentery. The parasites colonize the large intestine, but under some circumstances may invade the intestinal mucosa, enter the bloodstream and lead to the formation of abscesses such amoebic liver abscesses. The draft genome of E. histolytica, published in 2005, provided the scientific community with the first comprehensive view of the gene set for this parasite and important tools for elucidating the genetic basis of Entamoeba pathogenicity. Because complete genetic knowledge is critical for drug discovery and potential vaccine development for amoebiases, we have re-examined the original draft genome for E. histolytica. We have corrected the sequence assembly, improved the gene predictions and refreshed the functional gene assignments. As a result, this effort has led to a more accurate gene annotation, and the discovery of novel features, such as the presence of genome segmental duplications and the close association of some gene families with transposable elements. We believe that continuing efforts to improve genomic data will undoubtedly help to identify and characterize potential targets for amoebiasis control, as well as to contribute to a better understanding of genome evolution and pathogenesis for this parasite

Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution

The standard approach to analyzing 16S tag sequence data, which relies on clustering reads by sequence similarity into Operational Taxonomic Units (OTUs), underexploits the accuracy of modern sequencing technology. We present a clustering-free approach to multi-sample Illumina datasets that can identify independent bacterial subpopulations regardless of the similarity of their 16S tag sequences. Using published data from a longitudinal time-series study of human tongue microbiota, we are able to resolve within standard 97% similarity OTUs up to 20 distinct subpopulations, all ecologically distinct but with 16S tags differing by as little as 1 nucleotide (99.2% similarity). A comparative analysis of oral communities of two cohabiting individuals reveals that most such subpopulations are shared between the two communities at 100% sequence identity, and that dynamical similarity between subpopulations in one host is strongly predictive of dynamical similarity between the same subpopulations in the other host. Our method can also be applied to samples collected in cross-sectional studies and can be used with the 454 sequencing platform. We discuss how the sub-OTU resolution of our approach can provide new insight into factors shaping community assembly.Comment: Updated to match the published version. 12 pages, 5 figures + supplement. Significantly revised for clarity, references added, results not change

CodingQuarry: Highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts

Background: The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. Results: CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against whole genome Sc. pombe and S. cerevisiae annotations further substantiate a 4-5% improvement in the number of correctly predicted genes. Conclusions: We demonstrate the success of a novel method of incorporating RNA-seq data into GHMM fungal gene prediction. This shows that a high quality annotation can be achieved without relying on protein homology or a training set of genes. CodingQuarry is freely available (https://sourceforge.net/projects/codingquarry/), and suitable for incorporation into genome annotation pipelines

Extragalactic Results from the Infrared Space Observatory

More than a decade ago the IRAS satellite opened the realm of external galaxies for studies in the 10 to 100 micron band and discovered emission from tens of thousands of normal and active galaxies. With the 1995-1998 mission of the Infrared Space Observatory the next major steps in extragalactic infrared astronomy became possible: detailed imaging, spectroscopy and spectro-photometry of many galaxies detected by IRAS, as well as deep surveys in the mid- and far- IR. The spectroscopic data reveal a wealth of detail about the nature of the energy source(s) and about the physical conditions in galaxies. ISO's surveys for the first time explore the infrared emission of distant, high-redshift galaxies. ISO's main theme in extragalactic astronomy is the role of star formation in the activity and evolution of galaxies.Comment: 106 pages, including 17 figures. Ann.Rev.Astron.Astrophys. (in press), a gzip'd pdf file (667kB) is also available at http://www.mpe.mpg.de/www_ir/preprint/annrev2000.pdf.g

New AI Prediction Model Using Serial PT-INR Measurements in AF Patients on VKAs: GARFIELD-AF

Aims: Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from Global Anticoagulant Registry in the Field (GARFIELD)-AF registry, a new AI model was developed for predicting clinical outcomes in atrial fibrillation (AF) patients up to 1 year based on sequential measures of prothrombin time international normalized ratio (PT-INR) within 30 days of enrolment. Methods and results: Patients with newly diagnosed AF who were treated with vitamin K antagonists (VKAs) and had at least three measurements of PT-INR taken over the first 30 days after prescription were analysed. The AI model was constructed with multilayer neural network including long short-term memory and one-dimensional convolution layers. The neural network was trained using PT-INR measurements within days 0–30 after starting treatment and clinical outcomes over days 31–365 in a derivation cohort (cohorts 1–3; n = 3185). Accuracy of the AI model at predicting major bleed, stroke/systemic embolism (SE), and death was assessed in a validation cohort (cohorts 4–5; n = 1523). The model’s c-statistic for predicting major bleed, stroke/SE, and all-cause death was 0.75, 0.70, and 0.61, respectively. Conclusions: Using serial PT-INR values collected within 1 month after starting VKA, the new AI model performed better than time in therapeutic range at predicting clinical outcomes occurring up to 12 months thereafter. Serial PT-INR values contain important information that can be analysed by computer to help predict adverse clinical outcomes