Identification of a locus involved in the utilization of iron by Actinobacillus pleuropneumoniae

The cloned afu locus of Actinobacillus pleuropneumoniae restored the ability of an Escherichia coli K-12 mutant (aroB) to grow on iron-limited media. DNA sequence analysis of the fragment showed that there are three genes designated afuA, afuB and afuC (Actinobacillus ferric uptake) that encode products similar to the SfuABC proteins of Serratia marcescens, the HitABC proteins of Haemophilia influenzae, the FbpABC proteins of Neisseria gonorrhoeae and the YfuABC proteins of Yersinia enterocolitica. The three genes encode a periplasmic iron-binding protein (AfuA), a highly hydrophobic integral cytoplasmic membrane protein with two consensus permease motifs (AfuB) and one hydrophilic peripheral cytoplasmic membrane protein with Walker ATP-binding motifs (AfuC), respectively. This system has been shown to constitute a periplasmic binding protein-dependent iron transport system in these organisms. The afuABC operon is locating approximately 200 bp upstream of apxIC gene, but transcribed in opposite direction to the ApxI-toxin gene

Association between use of non–vitamin k oral anticoagulants with and without concurrent medications and risk of major bleeding in nonvalvular atrial fibrillation

Importance:  Non–vitamin K oral anticoagulants (NOACs) are commonly prescribed with other medications that share metabolic pathways that may increase major bleeding risk. Objective:  To assess the association between use of NOACs with and without concurrent medications and risk of major bleeding in patients with nonvalvular atrial fibrillation. Design, Setting, and Participants:  Retrospective cohort study using data from the Taiwan National Health Insurance database and including 91 330 patients with nonvalvular atrial fibrillation who received at least 1 NOAC prescription of dabigatran, rivaroxaban, or apixaban from January 1, 2012, through December 31, 2016, with final follow-up on December 31, 2016. Exposures:  NOAC with or without concurrent use of atorvastatin; digoxin; verapamil; diltiazem; amiodarone; fluconazole; ketoconazole, itraconazole, voriconazole, or posaconazole; cyclosporine; erythromycin or clarithromycin; dronedarone; rifampin; or phenytoin. Main Outcomes and Measures:  Major bleeding, defined as hospitalization or emergency department visit with a primary diagnosis of intracranial hemorrhage or gastrointestinal, urogenital, or other bleeding. Adjusted incidence rate differences between person-quarters (exposure time for each person during each quarter of the calendar year) of NOAC with or without concurrent medications were estimated using Poisson regression and inverse probability of treatment weighting using the propensity score. Results:  Among 91 330 patients with nonvalvular atrial fibrillation (mean age, 74.7 years [SD, 10.8]; men, 55.8%; NOAC exposure: dabigatran, 45 347 patients; rivaroxaban, 54 006 patients; and apixaban, 12 886 patients), 4770 major bleeding events occurred during 447 037 person-quarters with NOAC prescriptions. The most common medications co-prescribed with NOACs over all person-quarters were atorvastatin (27.6%), diltiazem (22.7%), digoxin (22.5%), and amiodarone (21.1%). Concurrent use of amiodarone, fluconazole, rifampin, and phenytoin with NOACs had a significant increase in adjusted incidence rates per 1000 person-years of major bleeding than NOACs alone: 38.09 for NOAC use alone vs 52.04 for amiodarone (difference, 13.94 [99% CI, 9.76-18.13]); 102.77 for NOAC use alone vs 241.92 for fluconazole (difference, 138.46 [99% CI, 80.96-195.97]); 65.66 for NOAC use alone vs 103.14 for rifampin (difference, 36.90 [99% CI, 1.59-72.22); and 56.07 for NOAC use alone vs 108.52 for phenytoin (difference, 52.31 [99% CI, 32.18-72.44]; P < .01 for all comparisons). Compared with NOAC use alone, the adjusted incidence rate for major bleeding was significantly lower for concurrent use of atorvastatin, digoxin, and erythromycin or clarithromycin and was not significantly different for concurrent use of verapamil; diltiazem; cyclosporine; ketoconazole, itraconazole, voriconazole, or posaconazole; and dronedarone. Conclusions and Relevance:  Among patients taking NOACs for nonvalvular atrial fibrillation, concurrent use of amiodarone, fluconazole, rifampin, and phenytoin compared with the use of NOACs alone, was associated with increased risk of major bleeding. Physicians prescribing NOAC medications should consider the potential risks associated with concomitant use of other drugs

Microbial Diagnostic Array Workstation (MDAW): a web server for diagnostic array data storage, sharing and analysis

<p>Abstract</p> <p>Background</p> <p>Microarrays are becoming a very popular tool for microbial detection and diagnostics. Although these diagnostic arrays are much simpler when compared to the traditional transcriptome arrays, due to the high throughput nature of the arrays, the data analysis requirements still form a bottle neck for the widespread use of these diagnostic arrays. Hence we developed a new online data sharing and analysis environment customised for diagnostic arrays.</p> <p>Methods</p> <p>Microbial Diagnostic Array Workstation (MDAW) is a database driven application designed in MS Access and front end designed in ASP.NET.</p> <p>Conclusion</p> <p>MDAW is a new resource that is customised for the data analysis requirements for microbial diagnostic arrays.</p

EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion

Panoptic segmentation is a scene parsing task which unifies semantic segmentation and instance segmentation into one single task. However, the current state-of-the-art studies did not take too much concern on inference time. In this work, we propose an Efficient Panoptic Segmentation Network (EPSNet) to tackle the panoptic segmentation tasks with fast inference speed. Basically, EPSNet generates masks based on simple linear combination of prototype masks and mask coefficients. The light-weight network branches for instance segmentation and semantic segmentation only need to predict mask coefficients and produce masks with the shared prototypes predicted by prototype network branch. Furthermore, to enhance the quality of shared prototypes, we adopt a module called "cross-layer attention fusion module", which aggregates the multi-scale features with attention mechanism helping them capture the long-range dependencies between each other. To validate the proposed work, we have conducted various experiments on the challenging COCO panoptic dataset, which achieve highly promising performance with significantly faster inference speed (53ms on GPU).Comment: ACCV 2020. Code is available at: https://github.com/neo85824/epsne