Isolation and Characterization of Adenoviruses Persistently Shed from the Gastrointestinal Tract of Non-Human Primates

Adenoviruses are important human pathogens that have been developed as vectors for gene therapies and genetic vaccines. Previous studies indicated that human infections with adenoviruses are self-limiting in immunocompetent hosts with evidence of some persistence in adenoid tissue. We sought to better understand the natural history of adenovirus infections in various non-human primates and discovered that healthy populations of great apes (chimpanzees, bonobos, gorillas, and orangutans) and macaques shed substantial quantities of infectious adenoviruses in stool. Shedding in stools from asymptomatic humans was found to be much less frequent, comparable to frequencies reported before. We purified and fully sequenced 30 novel adenoviruses from apes and 3 novel adenoviruses from macaques. Analyses of the new ape adenovirus sequences (as well as the 4 chimpanzee adenovirus sequences we have previously reported) together with 22 complete adenovirus genomes available from GenBank revealed that (a) the ape adenoviruses could clearly be classified into species corresponding to human adenovirus species B, C, and E, (b) there was evidence for intraspecies recombination between adenoviruses, and (c) the high degree of phylogenetic relatedness of adenoviruses across their various primate hosts provided evidence for cross species transmission events to have occurred in the natural history of B and E viruses. The high degree of asymptomatic shedding of live adenovirus in non-human primates and evidence for zoonotic transmissions warrants caution for primate handling and housing. Furthermore, the presence of persistent and/or latent adenovirus infections in the gut should be considered in the design and interpretation of human and non-human primate studies with adenovirus vectors

Simulation as a preoperative planning approach in advanced heart failure patients. A retrospective clinical analysis

Abstract Background Modelling and simulation may become clinically applicable tools for detailed evaluation of the cardiovascular system and clinical decision-making to guide therapeutic intervention. Models based on pressure–volume relationship and zero-dimensional representation of the cardiovascular system may be a suitable choice given their simplicity and versatility. This approach has great potential for application in heart failure where the impact of left ventricular assist devices has played a significant role as a bridge to transplant and more recently as a long-term solution for non eligible candidates. Results We sought to investigate the value of simulation in the context of three heart failure patients with a view to predict or guide further management. CARDIOSIM© was the software used for this purpose. The study was based on retrospective analysis of haemodynamic data previously discussed at a multidisciplinary meeting. The outcome of the simulations addressed the value of a more quantitative approach in the clinical decision process. Conclusions Although previous experience, co-morbidities and the risk of potentially fatal complications play a role in clinical decision-making, patient-specific modelling may become a daily approach for selection and optimisation of device-based treatment for heart failure patients. Willingness to adopt this integrated approach may be the key to further progress

Interior Point Methods for Combinatorial Optimization

Contents 1 Introduction 2 2 Combinatorial optimization 3 2.1 Examples of combinatorial optimization problems : : : : : : : : : : : : 3 2.2 Scope and computational efficiency : : : : : : : : : : : : : : : : : : : : 7 3 Solution techniques 13 3.1 Combinatorial approach : : : : : : : : : : : : : : : : : : : : : : : : : : 13 3.2 Continuous approach : : : : : : : : : : : : : : : : : : : : : : : : : : : : 14 3.2.1 Examples of embedding : : : : : : : : : : : : : : : : : : : : : : 14 3.2.2 Global approximation : : : : : : : : : : : : : : : : : : : : : : : 15 3.2.3 Continuous trajectories : : : : : : : : : : : : : : : : : : : : : : 15 3.2.4 Topological properties : : : : : : : : : : : : : : : : : : : : : : : 17 4 Interior point methods for linear and network programming 18 4.1 Linear programming : : : : : : : :