Robust multivariable control of complex biological processes

This paper addresses advanced control of a biological nutrient removal (BNR) activated sludge process. Based on a previously validated distributed parameter model of the BNR activated sludge process, we present robust multivariable controller designs for the process, involving loop shaping of plant model, robust stability and performance analyses. Results from three design case studies showed that a multivariable controller with stability margins of 0.163, 0.492 and 1.062 measured by the normalised coprime factor, multiplicative and additive uncertainties respectively give the best results for meeting performance robustness specifications. The controller robustly stabilises effluent nutrients in the presence of uncertainties with the behaviour of phosphorus accumulating organisms as well as to effectively attenuate major disturbances introduced as step changes. This study also shows that, performance of the multivariable robust controller is superior to multi-loops SISO PI controllers for regulating the BNR activated sludge process in terms of robust stability and performance and controlling the process using inlet feed flowrate is infeasible. (C) 2003 Elsevier Ltd. All rights reserved.

Neogenin: A multi-functional receptor regulating diverse developmental processes

Neogenin, a close relative of the axon guidance receptor Deleted in Colorectal Cancer (DCC), has been shown to be a receptor for members of the Netrin and Repulsive Guidance Molecule (RGM) families. While Netrin-l-Neogenin interactions result in a chernoattractive axon guidance response, the interaction between Neogenin and RGMa induces a chemorepulsive response. Evidence is now accumulating that Neogenin is a multi-functional receptor regulating many diverse developmental processes, including neural tube and mammary gland formation, myogenesis and angiogenesis. Little is known of the function of Neogenin in the adult, however, a novel role in the regulation of iron homeostasis is now emerging. While the signal transduction pathways activated by Neogenin are poorly understood, it is clear that the functional outcome of Neogenin activation, at least in the embryo, depends on both the developmental context as well as the nature of the ligand. (c) 2006 Elsevier Ltd. All rights reserved.

Genetic influences on cognitive processes associated with distraction: An event-related potential study of the slow wave

The efficiency of inhibitory control processes has been proposed as a mechanism constraining working-memory capacity. In order to investigate genetic influences on processes that may reflect interference control, event-related potential (ER-P) activity recorded at frontal sites, during distracting and nondistracting conditions of a working-memory task, in a sample of 509 twin pairs was examined. The ERP component of interest was the slow wave (SW). Considerable overlap in source of genetic influence was found, with a common genetic factor accounting for 37 - 45% of SW variance irrespective of condition. However, 3 - 8 % of SW variance in the distracting condition was influenced by an independent genetic source. These results suggest that neural responses to irrelevant and distracting information, that may disrupt working-memory performance, differ in a fundamental way from perceptual and memory-based processing in a working-memory task. Furthermore, the results are consistent with the view that cognition is a complex genetic trait influenced by numerous genes of small influence.

Mixed model formulations for multi-environment trials

When studying genotype X environment interaction in multi-environment trials, plant breeders and geneticists often consider one of the effects, environments or genotypes, to be fixed and the other to be random. However, there are two main formulations for variance component estimation for the mixed model situation, referred to as the unconstrained-parameters (UP) and constrained-parameters (CP) formulations. These formulations give different estimates of genetic correlation and heritability as well as different tests of significance for the random effects factor. The definition of main effects and interactions and the consequences of such definitions should be clearly understood, and the selected formulation should be consistent for both fixed and random effects. A discussion of the practical outcomes of using the two formulations in the analysis of balanced data from multi-environment trials is presented. It is recommended that the CP formulation be used because of the meaning of its parameters and the corresponding variance components. When managed (fixed) environments are considered, users will have more confidence in prediction for them but will not be overconfident in prediction in the target (random) environments. Genetic gain (predicted response to selection in the target environments from the managed environments) is independent of formulation.

Confirmatory factor analysis and psychometric properties of the Anxiety Sensitivity Index-Revised in clinical and normative populations

The present investigation aimed to critically examine the factor structure and psychometric properties of the Anxiety Sensitivity Index - Revised (ASI-R). Confirmatory factor analysis using a clinical sample of adults (N = 248) revealed that the ASI-R could be improved substantially through the removal of 15 problematic items in order to account for the most robust dimensions of anxiety sensitivity. This modified scale was renamed the 21-item Anxiety Sensitivity Index (21-item ASI) and reanalyzed with a large sample of normative adults (N = 435), revealing configural and metric invariance across groups. Further comparisons with other alternative models, using multi-sample analysis, indicated the 21-item ASI to be the best fitting model for both groups. There was also evidence of internal consistency, test-retest reliability, and construct validity for both samples suggesting that the 21-item ASI is a useful assessment device for investigating the construct of anxiety sensitivity in both clinical and normative populations.

Inhibition of transforming growth factor-beta 1 signaling attenuates ataxia telanglectasia mutated activity in response to genotoxic stress

Ionizing radiation causes DNA damage that elicits a cellular program of damage control coordinated by the kinase activity of ataxia telangiectasia mutated protein (ATM). Transforming growth factor beta (TGF beta)-1, which is activated by radiation, is a potent and pleiotropic mediator of physiologic and pathologic processes. Here we show that TGF beta inhibition impedes the canonical cellular DNA damage stress response. Irradiated Tgf beta 1 nail murine epithelial cells or human epithelial cells treated with a small-molecule inhibitor of TGF beta type I receptor kinase exhibit decreased phosphorylation of Chk2, Rad17, and p53; reduced gamma H2AX radiation-induced foci; and increased radiosensitivity compared with TGF beta competent cells. We determined that loss of TGF beta signaling in epithelial cells truncated ATM autophosphorylation and significantly reduced its kinase activity, without affecting protein abundance. Addition of TGF beta restored functional ATM and downstream DNA damage responses. These data reveal a heretofore undetected critical link between the microenvironment and ATM, which directs epithelial cell stress responses, cell fate, and tissue integrity. Thus, Tgf beta 1, in addition to its role in homoeostatic growth control, plays a complex role in regulating responses to genotoxic stress, the failure of which would contribute to the development of cancer; conversely, inhibiting TGF beta may be used to advantage in cancer therapy.

Development of a multi-tiered speech annotation system for accented English

This paper discusses methodological issues in the development of a multitiered, phonetic annotation system, intended to capture pronunciation variation in the speech of second language learners and to serve in construction of a data base for training ASR models to recognize major pronunciation variants in the assessment of accented English.

Parallel implementation of stochastic simulation for large scale cellular processes

Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes