976 resultados para Moore, Marcel


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With its foregrounding of the political issue of the denial of Aboriginal Australian sovereignty by British invaders in its big budget, mainstream narrative, 'The Sapphires' (Wayne Blair 2012) is shown to be another example of a "fourth formation" (Starrs 2012) in Moore and Muecke's 1985 model. Blair's feel-good movie features an all-Aboriginal Australian troupe of singers, The Sapphires, who undertake a journey of self-discovery whereby they learn the importance of choosing the protest songs of black Soul over the white coloniser's "whining" Country and Western songs and this is historically contextualised with a discussion of Aboriginal Australians and popular radio. Furthermore, this paper argues the iconic 'Welcome to Country' is twice subverted to reinforce this theme, firstly in the Cummeragunja pub and secondly in war-torn Vietnam. Finally, the prediction is made that a "fifth formation", in which seeking recognition of Aboriginal Australian sovereignty is no longer the goal because it has become the ongoing reality, will soon be the project of Australian film-makers as they celebrate this long overdue societal shift.

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This paper presents a formative measurement index to assess cloud enterprise systems success. The scale development procedure is based on Moore and Benbasat (1991), including newer scale development elements which focus on the creation and assessment of formative constructs. The data is analysed using SmartPLS with a sample of 103 IT decision makers. The results show that the perception of net benefits is shaped not only by enterprise-system-specific factors like productivity improvements and higher quality of business processes, but also by factors which are specifically attributed to cloud systems, such as higher strategic flexibility. Reliability, user requirements and customization contribute most to the overall perception of system quality. Information quality shows no cloud-specific facets and is robust in the context of cloud enterprise systems.

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The myofibrillar protein synthesis (MPS) response to resistance exercise (REX) and protein ingestion during energy deficit (ED) is unknown. We determined, in young men (n=8) and women (n=7), protein signaling, resting post-absorptive MPS during energy balance [EB: 45 kcal∙(kg FFM∙d)-1] and after 5d of ED [30 kcal∙(kg FFM∙d)-1] as well as MPS while in ED after acute REX in the fasted state and with the ingestion of whey protein (15 and 30 g). Post-absorptive rates of MPS were 27% lower in ED than EB (P<0.001), but REX stimulated MPS to rates equal to EB. Ingestion of 15 and 30 g of protein after REX in ED increased MPS ~16 and ~34% above resting EB, (P<0.02). p70 S6Kthr389 phosphorylation increased above EB only with combined exercise and protein intake (~2-7 fold; P<0.05). In conclusion, short-term ED reduces post-absorptive MPS, however, a bout of REX in ED restores MPS to values observed at rest in EB. The ingestion of protein after REX further increases MPS above resting EB in a dose-dependent manner. We conclude that combining REX with increased protein availability after exercise enhances rates of skeletal muscle protein synthesis during short term ED and could, in the long term, preserve muscle mass.

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Published information on the incidence of pathogens in the field and laboratory infections of Hypsipyla spp. with entomopathogens is reviewed. In addition, some preliminary results of field collections from Ghana and Costa Rica are presented. Fungal pathogens from the Deuteromycetes have been isolated from both H. robusta Moore and H. grandella Zeller. Mermithid nematodes, Hexamermis spp., have been frequently isolated from larvae in the field and incidence of infection with these pathogens can reach significant levels. Microsporidia have been found in cadavers of larvae collected in the field but none have been identified so far. A number of pathogens of other Lepidoptera have been shown to be infectious to H. grandella , including Bacillus thuringiensis , Deuteromycete fungi and a nucleopolyhedrovirus (NPV) from Autographa californica . Hypsipyla spp. are difficult targets for microbial control, since the larvae are cryptic, occur at low density and occur sporadically. In addition, there is a low damage threshold, the plant is susceptible for a number of years and the susceptible part of the plant will rapidly outgrow any surface application. Key features of the biology of entomopathogens with relevance to the control of low density and cryptic pests are discussed. In the light of this experience, we discuss strategies to improve the possibilities of microbial control of this pest and suggest areas for research.

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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.

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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.

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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 ...

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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.

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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.

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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 ...

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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.