No abstract

The role of atypical bacteria and the effect of antibiotic treatments in acute bronchitis are still not clear. This study was conducted at 22 hospitals (17 primary care clinics and 5 university hospitals) in Korea. Outpatients (aged ≥ 18 yr) who had an acute illness with a new cough and sputum (≤ 30 days) were enrolled in 2013. Multiplex real-time polymerase chain reaction (RT-PCR) was used to detect five atypical bacteria. A total of 435 patients were diagnosed as having acute bronchitis (vs. probable pneumonia, n = 75), and 1.8% (n = 8) were positive for atypical pathogens (Bordetella pertussis, n = 3; B. parapertussis, n = 0; Mycoplasma pneumoniae, n = 1; Chlamydophila pneumoniae, n = 3; Legionella pneumophila, n = 1). Among clinical symptoms and signs, only post-tussive vomiting was more frequent in patients with atypical pathogens than those without (P = 0.024). In all, 72.2% of the enrolled patients received antibiotic treatment at their first visits, and β-lactams (29.4%) and quinolones (20.5%) were the most commonly prescribed agents. In conclusion, our study demonstrates that the incidence of atypical pathogens is low in patients with acute bronchitis, and the rate of antibiotic prescriptions is high.Graphical Abstract

No abstract

No abstract

We propose a fully-convolutional conditional generative model, the latent transformation neural network (LTNN), capable of view synthesis using a light-weight neural network suited for real-time applications. In contrast to existing conditional generative models which incorporate conditioning information via concatenation, we introduce a dedicated network component, the conditional transformation unit (CTU), designed to learn the latent space transformations corresponding to specified target views. In addition, a consistency loss term is defined to guide the network toward learning the desired latent space mappings, a task-divided decoder is constructed to refine the quality of generated views, and an adaptive discriminator is introduced to improve the adversarial training process. The generality of the proposed methodology is demonstrated on a collection of three diverse tasks: multi-view reconstruction on real hand depth images, view synthesis of real and synthetic faces, and the rotation of rigid objects. The proposed model is shown to exceed state-of-the-art results in each category while simultaneously achieving a reduction in the computational demand required for inference by 30% on average.