Adaptive resistance to biocides in Salmonella enterica and Escherichia coli O157 and cross-resistance to antimicrobial agents

The mechanisms by which bacteria resist killing by antibiotics and biocides are still poorly defined, although repeated exposure to sublethal concentrations of antibacterial agents undoubtedly contributes to their development. This study aimed both to investigate the potential of Salmonella enterica and Escherichia coli O157 for adaptive resistance to commonly used biocides and to determine any cross-resistance to antibiotics. Strains were repeatedly passaged in media containing increasing concentrations of a biocide or antibiotic until adaptive resistance was obtained. A wide panel of antimicrobial agents was then screened by using the adapted strain to determine cross-resistance, if any. Adaptive resistance was readily achieved for both S. enterica and E. coli O157. Cross-resistance in adaptively resistant S. enterica varied with the serotype; Salmonella enterica serovar Enteritidis expressed cross-resistance to chloramphenicol, whereas Salmonella enterica serovar Typhimurium expressed cross-resistance to chlorhexidine. Benzalkonium chloride-resistant Salmonella enterica serovar Virchow showed elevated resistance to chlorhexidine; however, chlorhexidine-resistant Salmonella serovar Virchow did not demonstrate reciprocal cross-resistance to benzalkonium chloride, suggesting specific rather than generic resistance mechanisms. E. coli O157 strains acquired high levels of resistance to triclosan after only two sublethal exposures and, when adapted, repeatedly demonstrated decreased susceptibilities to various antimicrobial agents, including chloramphenicol, erythromycin, imipenem, tetracycline, and trimethoprim, as well as to a number of biocides. These observations raise concern over the indiscriminate and often inappropriate use of biocides, especially triclosan, in situations where they are unnecessary, whereby they may contribute to the development of microbial resistance mechanisms.

Stochastic control strategies and adaptive critic methods

Adaptive critic methods have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, nonlinear and nonstationary environments. In this study, a novel probabilistic dual heuristic programming (DHP) based adaptive critic controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) adaptive critic method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterized by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the critic network is then calculated and shown to be equal to the analytically derived correct value.

Static, dynamic, and adaptive heterogeneity in distributed smart camera networks

We study heterogeneity among nodes in self-organizing smart camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when cameras use the same strategy, with heterogeneous configurations, when cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes that are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: nonadaptive mixed strategies and adaptive strategies, which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralized online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic, and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organization.

Effective video content distribution by combining TCP with adaptive FEC coding

This paper will look at the benefits and limitations of content distribution using Forward Error Correction (FEC) in conjunction with the Transmission Control Protocol (TCP). FEC can be used to reduce the number of retransmissions which would usually result from a lost packet. The requirement for TCP to deal with any losses is then greatly reduced. There are however side-effects to using FEC as a countermeasure to packet loss: an additional requirement for bandwidth. When applications such as real-time video conferencing are needed, delay must be kept to a minimum, and retransmissions are certainly not desirable. A balance, therefore, between additional bandwidth and delay due to retransmissions must be struck. Our results show that the throughput of data can be significantly improved when packet loss occurs using a combination of FEC and TCP, compared to relying solely on TCP for retransmissions. Furthermore, a case study applies the result to demonstrate the achievable improvements in the quality of streaming video perceived by end users.