Quorum sensing-induced phenotypic switching as a regulatory nutritional stress response in a competitive two-species biofilm: An individual-based cellular automata model

Competition for nutrients in a polymicrobial biofilm may lead to susceptible species being subjected to nutritionalstress. The influence of bacterial growth rates and interspecies interactions on their susceptibility and response tonutritional stress is not well understood. Pseudomonas aeruginosa and Staphylococcus aureus are two prevalentcausative pathogens that coexist in biofilm-associated infections. Despite being the slower-growing species, P.aeruginosa dominates in a two-species biofilm by inducing phenotypic switching of S. aureusto a metabolicallychallenged small colony variant (SCV) via the release of 2-heptyl-4-hydroxyquinoline N-oxide (HQNO). Wehypothesize that P. aeruginosa experiences nutritional stress in competition with S. aureus, and that the release ofHQNO is an adaptive response to nutritional stress. We present an individual-based two-species biofilm model inwhich interactions between entities induce emergent properties. As the biofilm matured, the difference in growthrates of the two species caused a non-uniform distribution of nutrients leading to nutritional stress for P. aeruginosa and a concurrent increase in the proportion of S. aureus subpopulation. The latter resulted in increasedrelease of autoinducer, and subsequently the upregulation of P. aeruginosa cells via quorum sensing. UpregulatedP. aeruginosa cells released HQNO at enhanced rates, thereby inducing phenotypic switching of S. aureus toSCVs which consume nutrient at a reduced rate. This shifted the nutrient distribution back in favor of P.aeruginosa, thereby relieving nutritional stress. Increase in nutritional stress potentiated the transformation of S.aureus into SCVs. HQNO production decreased once nutritional stress was relieved, indicating that phenotypicswitching acts as a regulatory stress-adaptive response.

Simulating the behavior of patients who leave a public hospital emergency department without being seen by a physician: a cellular automaton and agent-based framework

The objective of this study was to develop an agent based modeling (ABM) framework to simulate the behavior of patients who leave a public hospital emergency department (ED) without being seen (LWBS). In doing so, the study complements computer modeling and cellular automata (CA) techniques to simulate the behavior of patients in an ED. After verifying and validating the model by comparing it with data from a real case study, the significance of four preventive policies including increasing number of triage nurses, fast-track treatment, increasing the waiting room capacity and reducing treatment time were investigated by utilizing ordinary least squares regression. After applying the preventing policies in ED, an average of 42.14% reduction in the number of patients who leave without being seen and 6.05% reduction in the average length of stay (LOS) of patients was reported. This study is the first to apply CA in an ED simulation. Comparing the average LOS before and after applying CA with actual times from emergency department information system showed an 11% improvement. The simulation results indicated that the most effective approach to reduce the rate of LWBS is applying fast-track treatment. The ABM approach represents a flexible tool that can be constructed to reflect any given environment. It is also a support system for decision-makers to assess the relative impact of control strategies.

Simulation of the epidemic of influenza A(H1N1)in a university using cel-lular automata model / 中国感染控制杂志

Objective To explore the application value of cellular automata(CA)in simulating the epidemic spread of out-break of influenza A(H1N1).Methods The publications regarding influenza A(H1N1)from January 2009 to March 2015 were collected from the China National Knowledge Infrastructure(CNKI),epidemiological data of H1N1 were retrieved ac-cording to inclusion criteria,the Matlab 7.0 software was adopted to construct CA model for simulating and analyzing the epidemic of H1N1 occurred in a university in Chongqing between October 12 and November 20,2009.Results There were a total of 17 820 students in this university,the epidemic of influenza lasted 40 days in 2009;When the parameter,the ef-fective infection rate was 0.04,the model of CA fit well,and gave estimate for basic reproduction number (R0 )1.202. Conclusion CA has certain reliability in simulating epidemics of airborne infectious diseases,it can provide reference for the prevention and control of disease.

Aging is not senescence: a short computer demonstration and implications for medical practice

INTRODUCTION: The discussion regarding the evolution of aging is almost as old as Darwinian Evolution Theory, but to date, it has remained one of biology's unresolved problems. One issue is how to reconcile natural selection, which is understood as a process that purges deleterious characteristics, with senescence, which seems to offer no advantages to the individual. METHOD: A computer simulation that illustrates an evolutionary mechanism for the development of senescence in populations is presented. DISCUSSION: In this article, we debate that two popular explanations for the existence of senescence, namely, (1) the removal of elders for the benefit of the species and (2) the progressive deterioration of the organic machine due to continuous use, are not correct. While human populations continue to age, it is important that the physician understands that senescence, here defined as the progressive impairment of an organism, does not necessarily accompany aging, which we here define as the mere passage of time. As such, we argue that certain processes that were originally assumed to be part of aging should have their status changed because they are actually diseases. Physicians often encounter situations that depend on a better understanding of what limitations senescence imposes on most living species. The concepts of aging (the unavoidable passage of time), senescence (progressive physiologic impairment), and senility (the pathological development of diseases), are discussed.

Dynamical Complexity in Cognitive Neural Networks

In the last twenty years an important effort in brain sciences, especially in cognitive science, has been the development of mathematical tool that can deal with the complexity of extensive recordings corresponding to the neuronal activity obtained from hundreds of neurons. We discuss here along with some historical issues, advantages and limitations of Artificial Neural Networks (ANN) that can help to understand how simple brain circuits work and whether ANN can be helpful to understand brain neural complexity.