Identification and control of a Pseudomonas spp (P. fulva and P. putida) bloodstream infection outbreak in a teaching hospital in Beijing, China

SummaryObjectivesAn outbreak of bacteremia caused by Pseudomonas spp (P. fulva and P. putida) was first identified in our hospital in the summer of 2010 and reoccurred in the following year. Based on the epidemiological data collected in these 2 years, we initiated an investigation on the source of the outbreak. The aim of this study was to report the results of the investigation, as well as the intervention strategies that resulted in successful control of the outbreak.MethodsAn infection control team was set up consisting of infectious disease specialists, microbiologists, infection control practitioners, and head nurses. The microbiology and medical records of case-patients with P. fulva or P. putida bloodstream infections were reviewed. Environmental samples and intravenous (IV) solutions from the wards and the pharmacy center were collected for culturing. The molecular characteristics of the bacterial isolates were studied by pulsed-field gel electrophoresis (PFGE). Strict infection control strategies were implemented.ResultsA total of 20 case-patients from five inpatient wards were identified during three summer seasons from 2010 to 2012. Nineteen of them recovered with proper antibiotics. Unfortunately one died from complications of heart failure. A total of 19 isolates of P. fulva and four of P. putida were identified, of which 20 were from blood, two from environmental surface samples from the hospital pharmacy, and one from an in-use compounded solution from a case-patient in the cardiology ward. Molecular analysis revealed that the P. fulva isolated from the in-use compounded solution (5% glucose solution containing insulin, isosorbide dinitrate, and potassium magnesium aspartate) and the environmental samples had the same PFGE type as the clinical isolates.ConclusionsThe investigation identified that contaminated IV solution was the source of the P. fulva bacteremia, which prompted us to implement intensified control measures that resulted in successful control of the outbreak

Identifying protein complexes from interaction networks based on clique percolation and distance restriction

Background: Identification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. Each protein might be subordinate multiple protein complexes in the real protein-protein interaction networks.Identifying overlapping protein complexes from protein-protein interaction networks is a considerable research topic. Result: As an effective algorithm in identifying overlapping module structures, clique percolation method (CPM) has a wide range of application in social networks and biological networks. However, the recognition accuracy of algorithm CPM is lowly. Furthermore, algorithm CPM is unfit to identifying protein complexes with meso-scale when it applied in protein-protein interaction networks. In this paper, we propose a new topological model by extending the definition of k-clique community of algorithm CPM and introduced distance restriction, and develop a novel algorithm called CP-DR based on the new topological model for identifying protein complexes. In this new algorithm, the protein complex size is restricted by distance constraint to conquer the shortcomings of algorithm CPM. The algorithm CP-DR is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Conclusion: The proposed algorithm CP-DR based on clique percolation and distance restriction makes it possible to identify dense subgraphs in protein interaction networks, a large number of which correspond to known protein complexes. Compared to algorithm CPM, algorithm CP-DR has more outstanding performance

An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards

In many domains such as transportation and logistics, search and rescue, or cooperative surveillance, tasks are pending to be allocated with the consideration of possible execution uncertainties. Existing task coordination algorithms either ignore the stochastic process or suffer from the computational intensity. Taking advantage of the weakly coupled feature of the problem and the opportunity for coordination in advance, we propose a decentralized auction-based coordination strategy using a newly formulated score function which is generated by forming the problem into task-constrained Markov decision processes (MDPs). The proposed method guarantees convergence and at least 50% optimality in the premise of a submodular reward function. Furthermore, for the implementation on large-scale applications, an approximate variant of the proposed method, namely Deep Auction, is also suggested with the use of neural networks, which is evasive of the troublesome for constructing MDPs. Inspired by the well-known actor-critic architecture, two Transformers are used to map observations to action probabilities and cumulative rewards respectively. Finally, we demonstrate the performance of the two proposed approaches in the context of drone deliveries, where the stochastic planning for the drone league is cast into a stochastic price-collecting Vehicle Routing Problem (VRP) with time windows. Simulation results are compared with state-of-the-art methods in terms of solution quality, planning efficiency and scalability.Comment: 17 pages, 5 figure