This pooled analysis of double-blind, randomized, placebo-controlled trials aimed to investigate the impact of DOxofylline compaRed tO THEOphylline (DOROTHEO 1 and DOROTHEO 2 studies) on functional and clinical outcomes in asthma. Asthmatic patients ≥16 years of age with forced expiratory volume in 1 s (FEV) ≥50% and <80% and with ≥15% post-bronchodilator increase in FEV were randomized in a 1:1:1:1 ratio in DOROTHEO 1 to receive doxofylline 200 mg, doxofylline 400 mg, theophylline 250 mg, or placebo; in DOROTHEO 2 patients were randomized in a 1:1:1 ratio to receive doxofylline 400 mg, theophylline 250 mg, or placebo. All double-blind treatments were taken orally with immediate release formulations and three times daily. Data evaluating the effect of doxofylline 400 mg, theophylline 250 mg and placebo on FEV, asthma events rate, use of salbutamol as rescue medication and adverse events (AEs) were pooled from both studies. The pooled-analysis of 483 patients demonstrated that both doxofylline 400 mg and theophylline 250 mg significantly increased FEV, reduced the rate of asthma events and use of salbutamol to relieve asthma symptoms compared to placebo (p< 0.01). No significant differences were detected between doxofylline 400 mg and theophylline 250 mg. Doxofylline 400 mg did not significantly (p > 0.05) increase the risk of AEs compared to placebo, conversely in patients treated with theophylline 250 mg the risk of AEs was significantly (p < 0.05) greater than in those that received placebo. We conclude that doxofylline seems to offer a promising alternative to theophylline with a superior efficacy/safety profile in the management of patients with asthma.

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This work addresses the coexistence problem for radar networks. Specifically, we model a network of cooperative, independent, and non-communicating radar nodes which must share resources within the network as well as with non-cooperative nearby emitters. We approach this problem using online Machine Learning (ML) techniques. Online learning approaches are specifically preferred due to the fact that each radar node has no prior knowledge of the environment nor of the positions of the other radar nodes, and due to the sequential nature of the problem. For this task we specifically select the multi-player multi-armed bandit (MMAB) model, which poses the problem as a sequential game, where each radar node in a network makes independent selections of center frequency and waveform with the same goal of improving tracking performance for the network as a whole. For accurate tracking, each radar node communicates observations to a fusion center on set intervals. The fusion center has knowledge of the radar node placement, but cannot communicate to the individual nodes fast enough for waveform control. Every radar node in the network must learn the behavior of the environment, which includes rewards, interferer behavior, and target behavior. Each independent and identical node must choose one of many waveforms to transmit in each Pulse Repetition Interval (PRI) while avoiding \emph{collisions} with other nodes and interference from the environment. The goal for the network as a whole is to minimize target tracking error, which relies on obtaining high SINR in each time step. Our contributions include a mathematical description of the MMAB framework adapted to the radar network scenario. We conclude with a simulation study of several different network configurations. Experimental results show that iterative, online learning using MMAB outperforms the more traditional sense-and-avoid (SAA) and fixed-allocation approaches.

We model a radar network as an adversarial bandit problem, where the environment pre-selects reward sequences for each of several actions available to the network. This excludes environments which vary rewards in response to the learner’s actions. Adversarial environments include those with third partyemitters which enter and exit the environment according to some criteria which does not depend on the radar network. The network consists of several independent radar nodes, which attempt to attain the highest possible SINR in each of many time steps. We show that in such an environment, simple sub-band selection algorithms are unable to consistently attain high SINR. However, through the use of adversarial multi-player bandit algorithms, a radar network can continue to track targets without a loss in tracking precision