Host-pathogen checkpoints and population bottlenecks in persistent and intracellular uropathogenic Escherichia coli bladder infection

Bladder infections affect millions of people yearly, and recurrent symptomatic infections (cystitis) are very common. The rapid increase in infections caused by multidrug-resistant uropathogens threatens to make recurrent cystitis an increasingly troubling public health concern. Uropathogenic Escherichia coli (UPEC) cause the vast majority of bladder infections. Upon entry into the lower urinary tract, UPEC face obstacles to colonization that constitute population bottlenecks, reducing diversity, and selecting for fit clones. A critical mucosal barrier to bladder infection is the epithelium (urothelium). UPEC bypass this barrier when they invade urothelial cells and form intracellular bacterial communities (IBCs), a process which requires type 1 pili. IBCs are transient in nature, occurring primarily during acute infection. Chronic bladder infection is common and can be either latent, in the form of the quiescent intracellular reservoir (QIR), or active, in the form of asymptomatic bacteriuria (ASB/ABU) or chronic cystitis. In mice, the fate of bladder infection, QIR, ASB, or chronic cystitis, is determined within the first 24 h of infection and constitutes a putative host–pathogen mucosal checkpoint that contributes to susceptibility to recurrent cystitis. Knowledge of these checkpoints and bottlenecks is critical for our understanding of bladder infection and efforts to devise novel therapeutic strategies.

Household food insecurity is associated with less physical activity among children and adults in the U.S. population

Background Household food insecurity and physical activity are each important public-health concerns in the United States, but the relation between them was not investigated thoroughly. Objective We wanted to examine the association between food insecurity and physical activity in the U.S. population. Methods Physical activity measured by accelerometry (PAM) and physical activity measured by questionnaire (PAQ) data from the NHANES 2003–2006 were used. Individuals aged <6 y or >65 y, pregnant, with physical limitations, or with family income >350% of the poverty line were excluded. Food insecurity was measured by the USDA Household Food Security Survey Module. Adjusted ORs were calculated from logistic regression to identify the association between food insecurity and adherence to the physical-activity guidelines. Adjusted coefficients were obtained from linear regression to identify the association between food insecurity with sedentary/physical-activity minutes. Results In children, food insecurity was not associated with adherence to physical-activity guidelines measured via PAM or PAQ and with sedentary minutes (P > 0.05). Food-insecure children did less moderate to vigorous physical activity than food-secure children (adjusted coefficient = −5.24, P = 0.02). In adults, food insecurity was significantly associated with adherence to physical-activity guidelines (adjusted OR = 0.72, P = 0.03 for PAM; and OR = 0.84, P < 0.01 for PAQ) but was not associated with sedentary minutes (P > 0.05). Conclusion Food-insecure children did less moderate to vigorous physical activity, and food-insecure adults were less likely to adhere to the physical-activity guidelines than those without food insecurity.

Adaptive estimation of HMM transition probabilities

This paper presents new schemes for recursive estimation of the state transition probabilities for hidden Markov models (HMM's) via extended least squares (ELS) and recursive state prediction error (RSPE) methods. Local convergence analysis for the proposed RSPE algorithm is shown using the ordinary differential equation (ODE) approach developed for the more familiar recursive output prediction error (RPE) methods. The presented scheme converges and is relatively well conditioned compared with the ...

On adaptive HMM state estimation

In this paper new online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods. The best of the new schemes exploit the idempotent nature of Markov chains and work with a least squares prediction error index, using a posterior estimates, more suited to Markov models then traditionally used in identification of linear systems.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Optimal HMM processing of differentially phase modulated communication systems

This paper investigates demodulation of differentially phase modulated signals DPMS using optimal HMM filters. The optimal HMM filter presented in the paper is computationally of order N3 per time instant, where N is the number of message symbols. Previously, optimal HMM filters have been of computational order N4 per time instant. Also, suboptimal HMM filters have be proposed of computation order N2 per time instant. The approach presented in this paper uses two coupled HMM filters and exploits knowledge of ...

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

Faster parameter estimation using risk-sensitive filters

In this paper, we propose a risk-sensitive approach to parameter estimation for hidden Markov models (HMMs). The parameter estimation approach considered exploits estimation of various functions of the state, based on model estimates. We propose certain practical suboptimal risk-sensitive filters to estimate the various functions of the state during transients, rather than optimal risk-neutral filters as in earlier studies. The estimates are asymptotically optimal, if asymptotically risk neutral, and can give significantly improved transient performance, which is a very desirable objective for certain engineering applications. To demonstrate the improvement in estimation simulation studies are presented that compare parameter estimation based on risk-sensitive filters with estimation based on risk-neutral filters.

Optimal HMM filtering and decision feedback equalisation for differential encoded transmission systems

In this paper conditional hidden Markov model (HMM) filters and conditional Kalman filters (KF) are coupled together to improve demodulation of differential encoded signals in noisy fading channels. We present an indicator matrix representation for differential encoded signals and the optimal HMM filter for demodulation. The filter requires O(N3) calculations per time iteration, where N is the number of message symbols. Decision feedback equalisation is investigated via coupling the optimal HMM filter for estimating the message, conditioned on estimates of the channel parameters, and a KF for estimating the channel states, conditioned on soft information message estimates. The particular differential encoding scheme examined in this paper is differential phase shift keying. However, the techniques developed can be extended to other forms of differential modulation. The channel model we use allows for multiplicative channel distortions and additive white Gaussian noise. Simulation studies are also presented.

The effect of work-integrated learning in highlighting the complexity of work : a pilot study of work self-efficacy

This pilot study aims to examine the effect of work-integrated learning (WIL) on work self-efficacy (WSE) for undergraduate students from the Queensland University of Technology. A WSE instrument was used to examine the seven subscales of WSE. These were; learning, problem solving, pressure, role expectations, team work, sensitivity and work politics. The results of this pilot study revealed that, overall the WSE scores were highest when the students’ did not participate in the WIL unit (comparison group) in comparison to the WIL group. The current paper suggests that WSE scores were changed as a result of WIL participation. These findings open a new path for future studies allowing them to explore the relationship between WIL and the specific subscales of WSE.

Selection procedures for module discovery : exploring evolutionary algorithms for cognitive science

Evolutionary algorithms are playing an increasingly important role as search methods in cognitive science domains. In this study, methodological issues in the use of evolutionary algorithms were investigated via simulations in which procedures were systematically varied to modify the selection pressures on populations of evolving agents. Traditional roulette wheel, tournament, and variations of these selection algorithms were compared on the “needle-in-a-haystack” problem developed by Hinton and Nowlan in their 1987 study of the Baldwin effect. The task is an important one for cognitive science, as it demonstrates the power of learning as a local search technique in smoothing a fitness landscape that lacks gradient information. One aspect that has continued to foster interest in the problem is the observation of residual learning ability in simulated populations even after long periods of time. Effective evolutionary algorithms balance their search effort between broad exploration of the search space and in-depth exploitation of promising solutions already found. Issues discussed include the differential effects of rank and proportional selection, the tradeoff between migration of populations towards good solutions and maintenance of diversity, and the development of measures that illustrate how each selection algorithm affects the search process over generations. We show that both roulette wheel and tournament algorithms can be modified to appropriately balance search between exploration and exploitation, and effectively eliminate residual learning in this problem.

Supporting the needs of public health decision-makers and review authors in the UK

Introduction Systematic reviews, through the synthesis of multiple primary research studies, can be powerful tools in enabling evidence-informed public health policy debate, development and action. In seeking to optimize the utility of these reviews, it is important to understand the needs of those using them. Previous work has emphasized that researchers should adopt methods that are appropriate to the problems that public health decision-makers are grappling with, as well as to the policy context in which they operate.1,2 Meeting these demands poses significant methodological challenges for review authors and prompts a reconsideration of the resources, training and support structures available to facilitate the efficient and timely production of useful, comprehensive reviews. The Cochrane Public Health Group (CPHG) was formed in 2008 to support reviews of complex, upstream public health topics. The majority of CPHG authors are from the UK, which has historically been at the forefront of efforts to promote the production and use of systematic reviews of research relevant to public health decision-makers. The UK therefore provides a suitably mature national context in which to examine (i) the current and future demands of decision-makers to increase the use, value and impact of evidence syntheses; (ii) the implications this has for the scope and methods of reviews and (iii) the required action to build and support capacity to conduct such reviews.