Optimal sampling strategy for estimation of spatial genetic structure in tree populations

Fine-scale spatial genetic structure (SGS) in natural tree populations is largely a result of restricted pollen and seed dispersal. Understanding the link between limitations to dispersal in gene vectors and SGS is of key interest to biologists and the availability of highly variable molecular markers has facilitated fine-scale analysis of populations. However, estimation of SGS may depend strongly on the type of genetic marker and sampling strategy (of both loci and individuals). To explore sampling limits, we created a model population with simulated distributions of dominant and codominant alleles, resulting from natural regeneration with restricted gene flow. SGS estimates from subsamples (simulating collection and analysis with amplified fragment length polymorphism (AFLP) and microsatellite markers) were correlated with the 'real' estimate (from the full model population). For both marker types, sampling ranges were evident, with lower limits below which estimation was poorly correlated and upper limits above which sampling became inefficient. Lower limits (correlation of 0.9) were 100 individuals, 10 loci for microsatellites and 150 individuals, 100 loci for AFLPs. Upper limits were 200 individuals, five loci for microsatellites and 200 individuals, 100 loci for AFLPs. The limits indicated by simulation were compared with data sets from real species. Instances where sampling effort had been either insufficient or inefficient were identified. The model results should form practical boundaries for studies aiming to detect SGS. However, greater sample sizes will be required in cases where SGS is weaker than for our simulated population, for example, in species with effective pollen/seed dispersal mechanisms.

Assessing moth migration and population structuring in Helicoverpa annigera (Lepidoptera : Noctuidae) at the regional scale: Example from the Darling Downs, Australia

Analysis of gene flow and migration of Helicoverpa armigera (Hubner) in a major cropping region of Australia identified substantial genetic structuring, migration events, and significant population genotype changes over the 38-mo sample period from November 1999 to January 2003. Five highly variable microsatellite markers were used to analyze 916 individuals from 77 collections across 10 localities in the Darling Downs. The molecular data indicate that in some years (e.g., April 2002-March 2003), low levels of H. armigera migration and high differentiation between populations occurred, whereas in other years (e.g., April 2001-March 2002), there were higher levels of adult moth movement resulting in little local structuring of populations. Analysis of populations in other Australian cropping regions provided insight into the quantity and direction of immigration of H. armigera adults into the Darling Downs growing region of Australia. These data provide evidence adult moth movement differs from season to season, highlighting the importance of studies in groups such as the Lepidoptera extending over consecutive years, because short-term sampling may be misleading when population dynamics and migration change so significantly. This research demonstrates the importance of maintaining a coordinated insecticide resistance management strategy, because in some years H. armigera populations may be independent within a region and thus significantly influenced by local management practices; however, periods with high migration will occur and resistance may rapidly spread.

Estimating variable returns to scale production frontiers with alternative stochastic assumptions

Two stochastic production frontier models are formulated within the generalized production function framework popularized by Zellner and Revankar (Rev. Econ. Stud. 36 (1969) 241) and Zellner and Ryu (J. Appl. Econometrics 13 (1998) 101). This framework is convenient for parsimonious modeling of a production function with returns to scale specified as a function of output. Two alternatives for introducing the stochastic inefficiency term and the stochastic error are considered. In the first the errors are added to an equation of the form h(log y, theta) = log f (x, beta) where y denotes output, x is a vector of inputs and (theta, beta) are parameters. In the second the equation h(log y,theta) = log f(x, beta) is solved for log y to yield a solution of the form log y = g[theta, log f(x, beta)] and the errors are added to this equation. The latter alternative is novel, but it is needed to preserve the usual definition of firm efficiency. The two alternative stochastic assumptions are considered in conjunction with two returns to scale functions, making a total of four models that are considered. A Bayesian framework for estimating all four models is described. The techniques are applied to USDA state-level data on agricultural output and four inputs. Posterior distributions for all parameters, for firm efficiencies and for the efficiency rankings of firms are obtained. The sensitivity of the results to the returns to scale specification and to the stochastic specification is examined. (c) 2004 Elsevier B.V. All rights reserved.

Cytokine gene polymorphisms in idiopathic pulmonary fibrosis

Pro- and anti-fibrotic cytokine gene polymorphisms may affect expression of idiopathic pulmonary fibrosis (IPF). The aims of the present case-control study were to examine polymorphisms in the IL-6, transforming growth factor (TGF)-beta1, tumour necrosis factor (TNF)-alpha and interleukin-1 (IL-1)Ra genes in patients with IPF (n=22)-compared to healthy controls (n=140). Genotyping was performed on DNA extracted from peripheral blood lymphocytes, using polymerase chain reaction-restriction fragment length polymorphism with gene polymorphisms determined according to-published techniques. The following sites were examined: (i) IL-1Ra*1-5 (86 bp variable tandem repeat intron 2), (ii) IL-6 (-174G>C), (iii) TNF-alpha (-308G>A) and (iv) TGF-beta1 (Arg25Pro). The TNF-alpha (-308 A) allele was over-represented in the IPF (p(corr)=0.004) group compared to controls. Risk of IPF was significant for heterozygotes for: (i) the TNF-alpha (-308 A) allele (A/G) (odds ratio (OR) 2.9; 95% confidence interval (CI) 1.2-7.2; P=0.02), (ii) homozygotes (A/A) (OR 13.9; 95%CI 1.2-160; P=0.04) and (iii) carriage of the allele (A/A+A/G) (OR 4; 95%CI 1.6-10.2; P=0.003). The distribution of alleles and genotypes for IL-6, TGF-beta1 and IL-1Ra between the two groups was not significantly different. This is the third study to independently confirm that there is a significant association of the TNF-alpha (-308 A) allele with IPF. Further research is needed to assess the utility of cytokine gene polymorphisms as markers of disease-susceptibility.

Optimal control and operation of drum granulation processes

Process optimisation and optimal control of batch and continuous drum granulation processes are studied in this paper. The main focus of the current research has been: (i) construction of optimisation and control relevant, population balance models through the incorporation of moisture content, drum rotation rate and bed depth into the coalescence kernels; (ii) investigation of optimal operational conditions using constrained optimisation techniques; (iii) development of optimal control algorithms based on discretized population balance equations; and (iv) comprehensive simulation studies on optimal control of both batch and continuous granulation processes. The objective of steady state optimisation is to minimise the recycle rate with minimum cost for continuous processes. It has been identified that the drum rotation-rate, bed depth (material charge), and moisture content of solids are practical decision (design) parameters for system optimisation. The objective for the optimal control of batch granulation processes is to maximize the mass of product-sized particles with minimum time and binder consumption. The objective for the optimal control of the continuous process is to drive the process from one steady state to another in a minimum time with minimum binder consumption, which is also known as the state-driving problem. It has been known for some time that the binder spray-rate is the most effective control (manipulative) variable. Although other possible manipulative variables, such as feed flow-rate and additional powder flow-rate have been investigated in the complete research project, only the single input problem with the binder spray rate as the manipulative variable is addressed in the paper to demonstrate the methodology. It can be shown from simulation results that the proposed models are suitable for control and optimisation studies, and the optimisation algorithms connected with either steady state or dynamic models are successful for the determination of optimal operational conditions and dynamic trajectories with good convergence properties. (c) 2005 Elsevier Ltd. All rights reserved.

Dispersion and size control of layered double hydroxide nanoparticles in aqueous solutions

We report a simple but efficient method to prepare stable homogeneous suspensions containing monodispersed MgAl layered double hydroxide (LDH) nanoparticles that have wide promising applications in cellular drug ( gene) delivery, polymer/LDH nanocomposites, and LDH thin films for catalysis, gas separation, sensing, and electrochemical materials. This new method involves a fast coprecipitation followed by controlled hydrothermal treatment under different conditions and produces stable homogeneous LDH suspensions under variable hydrothermal treatment conditions. Moreover, the relationship between the LDH particle size and the hydrothermal treatment conditions ( time, temperature, and concentration) has been systematically investigated, which indicates that the LDH particle size can be precisely controlled between 40 and 300 nm by adjusting these conditions. The reproducibility of making the identical suspensions under identical conditions has been confirmed with a number of experiments. The dispersion of agglomerated LDH aggregates into individual LDH crystallites during the hydrothermal treatment has been further discussed. This method has also been successfully applied to preparing stable homogeneous LDH suspensions containing various other metal ions such as Ni2+, Fe2+, Fe3+, Co2+, Cd2+, and Gd3+ in the hydroxide layers and many inorganic anions such as Cl-, CO32-, NO3-, and SO42-.

Health-protective behaviours and risk of fall-related hip fractures: a population-based case-control study

Background: fall-related hip fractures are one of the most common causes of disability and mortality in older age. The study aimed to quantify the relationship between lifestyle behaviours and the risk of fall-related hip fracture in community-dwelling older people. The purpose was to contribute evidence for the promotion of healthy ageing as a population-based intervention for falls injury prevention. Methods: a case-control study was conducted with 387 participants, with a case-control ratio of 1:2. Incident cases of fall-related hip fracture in people aged 65 and over were recruited from six hospital sites in Brisbane, Australia, in 2003-04. Community-based controls, matched by age, sex and postcode, were recruited via electoral roll sampling. A questionnaire designed to assess lifestyle risk factors, identified as determinants of healthy ageing, was administered at face-to-face interviews. Results: behavioural factors which had a significant independent protective effect on the risk of hip fracture included never smoking [adjusted odds ratio (AOR): 0.33 (0.12-0.88)], moderate alcohol consumption in mid- and older age [AOR: 0.49 (0.25-0.95)], not losing weight between mid- and older age [AOR: 0.36 (0.20-0.65)], playing sport in older age [AOR: 0.49 (0.29-0.83)] and practising a greater number of preventive medical care [AOR: 0.54 (0.32-0.94)] and self-health behaviours [AOR: 0.56 (0.33-0.94)]. Conclusion: with universal exposures, clear associations and modifiable behavioural factors, this study has contributed evidence to reduce the major public health burden of fall-related hip fractures using readily implemented population-based healthy ageing strategies.

Hypoxic/ischemic models in newborn piglet: Comparison of constant FiO(2) versus variable FiO(2) delivery

A comparison of a constant (continuous delivery of 4% FiO(2)) and a variable (initial 5% FiO(2) with adjustments to induce low amplitude EEG (LAEEG) and hypotension) hypoxic/ischemic insult was performed to determine which insult was more effective in producing a consistent degree of survivable neuropathological damage in a newborn piglet model of perinatal asphyxia. We also examined which physiological responses contributed to this outcome. Thirty-nine 1-day-old piglets were subjected to either a constant hypoxic/ischemic insult of 30- to 37-min duration or a variable hypoxic/ischemic insult of 30-min low peak amplitude EEG (LAEEG < 5 mu V) including 10 min of low mean arterial blood pressure (MABP < 70% of baseline). Control animals (n = 6) received 21% FiO(2) for the duration of the experiment. At 72 h, the piglets were euthanased, their brains removed and fixed in 4% paraformaldehyde and assessed for hypoxic/ischemic injury by histological analysis. Based on neuropathology scores, piglets were grouped as undamaged or damaged; piglets that did not survive to 72 h were grouped separately as dead. The variable insult resulted in a greater number of piglets with neuropathological damage (undamaged = 12.5%, damaged = 68.75%, dead = 18.75%) while the constant insult resulted in a large proportion of undamaged piglets (undamaged = 50%, damaged = 22.2%, dead = 27.8%). A hypoxic insult varied to maintain peak amplitude EEG < 5 mu V results in a greater number of survivors with a consistent degree of neuropathological damage than a constant hypoxic insult. Physiological variables MABP, LAEEG, pH and arterial base excess were found to be significantly associated with neuropathological outcome. (c) 2006 Elsevier B.V. All rights reserved.

Improved algorithms for rare event simulation with heavy tails

The estimation of P(S-n > u) by simulation, where S, is the sum of independent. identically distributed random varibles Y-1,..., Y-n, is of importance in many applications. We propose two simulation estimators based upon the identity P(S-n > u) = nP(S, > u, M-n = Y-n), where M-n = max(Y-1,..., Y-n). One estimator uses importance sampling (for Y-n only), and the other uses conditional Monte Carlo conditioning upon Y1,..., Yn-1. Properties of the relative error of the estimators are derived and a numerical study given in terms of the M/G/1 queue in which n is replaced by an independent geometric random variable N. The conclusion is that the new estimators compare extremely favorably with previous ones. In particular, the conditional Monte Carlo estimator is the first heavy-tailed example of an estimator with bounded relative error. Further improvements are obtained in the random-N case, by incorporating control variates and stratification techniques into the new estimation procedures.

Pre-term delivery and periodontal disease: a case-control study from Croatia

Aim: The aim of this report was to assess the strength and influence of periodontitis as a possible risk factor for pre-term birth (PTB) in a cohort of 81 primiparous Croatian mothers aged 18-39 years. Methods: PTB cases (n=17; mean age 25 +/- 2.9 years; age range 20-33 years) were defined as spontaneous delivery after less than 37 completed weeks of gestation that were followed by spontaneous labour or spontaneous rupture of membranes. Controls (full-time births) were normal births at or after 37 weeks of gestation (n=64; mean age 25 +/- 2.9 years; age range 19-39 years). Information on known risk factors and obstetric factors included the current pregnancy history, maternal age at delivery, pre-natal care, nutritional status, tobacco use, alcohol use, genitourinary infections, vaginosis, gestational age, and birth weight. Full-mouth periodontal examination was performed on all mothers within 2 days of delivery. Results: PTB cases had significantly worse periodontal status than controls (p=0.008). Multivariate logistic regression model, after controlling for other risk factors, demonstrated that periodontal disease is a significant independent risk factor for PTB, with an adjusted odds ratio of 8.13 for the PTB group (95% confidence interval 2.73-45.9). Conclusion: Periodontal disease represents a strong, independent, and clinically significant risk factor for PTB in the studied cohort. There are strong indicators that periodontal therapy should form a part of preventive prenatal care in Croatia.

Functional analysis of a real-time protocol for networked control systems

Traditional real-time control systems are tightly integrated into the industrial processes they govern. Now, however, there is increasing interest in networked control systems. These provide greater flexibility and cost savings by allowing real-time controllers to interact with industrial processes over existing communications networks. New data packet queuing protocols are currently being developed to enable precise real-time control over a network with variable propagation delays. We show how one such protocol was formally modelled using timed automata, and how model checking was used to reveal subtle aspects of the control system's dynamic behaviour.

Point-wise confidence interval estimation by neural networks: A comparative study based on automotive engine calibration.

In developing neural network techniques for real world applications it is still very rare to see estimates of confidence placed on the neural network predictions. This is a major deficiency, especially in safety-critical systems. In this paper we explore three distinct methods of producing point-wise confidence intervals using neural networks. We compare and contrast Bayesian, Gaussian Process and Predictive error bars evaluated on real data. The problem domain is concerned with the calibration of a real automotive engine management system for both air-fuel ratio determination and on-line ignition timing. This problem requires real-time control and is a good candidate for exploring the use of confidence predictions due to its safety-critical nature.

Improved robust control of nonlinear stochastic systems using uncertain models

We introduce a technique for quantifying and then exploiting uncertainty in nonlinear stochastic control systems. The approach is suboptimal though robust and relies upon the approximation of the forward and inverse plant models by neural networks, which also estimate the intrinsic uncertainty. Sampling from the resulting Gaussian distributions of the inversion based neurocontroller allows us to introduce a control law which is demonstrably more robust than traditional adaptive controllers.

Multi-valued control problems and mixture density network

We have proposed a novel robust inversion-based neurocontroller that searches for the optimal control law by sampling from the estimated Gaussian distribution of the inverse plant model. However, for problems involving the prediction of continuous variables, a Gaussian model approximation provides only a very limited description of the properties of the inverse model. This is usually the case for problems in which the mapping to be learned is multi-valued or involves hysteritic transfer characteristics. This often arises in the solution of inverse plant models. In order to obtain a complete description of the inverse model, a more general multicomponent distributions must be modeled. In this paper we test whether our proposed sampling approach can be used when considering an arbitrary conditional probability distributions. These arbitrary distributions will be modeled by a mixture density network. Importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The effectiveness of the importance sampling from an arbitrary conditional probability distribution will be demonstrated using a simple single input single output static nonlinear system with hysteretic characteristics in the inverse plant model.