A morphological model for sexing nestling peregrine falcons (Falco Peregrinus Macropus) verified through genetic analysis

Adult peregrine falcons (Falco peregrinus macropus) have monotypic plumage and display strong reversed sexual dimorphism, with females significantly larger than males. Reversed sexual dimorphism is measurable among nestlings in the latter stages of their development and can therefore be used to differentiate between sexes. In the early stages of development, however, nestlings cannot be sexed with any degree of certainty because morphological differentiation between the sexes is not well developed. During this study we developed a model for sexing younger nestlings based on genetic analysis and morphometric data collected as part of a long-term banding study of this species. A discriminant function model based on morphological characteristics was developed for determining the sex of nestlings (n = 150) in the field and was shown to be 96.0% accurate. This predictive model was further tested against an independent morphometric dataset taken from a second group of nestlings (n = 131). The model correctly allocated sex to 96.2% of this second group of nestlings. Sex can reliably be determined (98.6% accurate) for nestlings that have a wing length of at least 9 cm using this model. Application of this model, therefore, allows the banding of younger nestlings and, as such, significantly increases the period of time over which banding can occur. Another important implication of this model is that by banding nestlings earlier, they are less likely to jump from the nest, therefore reducing the risk of injury to both the brood and the bander.

Forecasting inflation, industrial output and exchange rates : a template study for India

This paper reports results from a forecasting study for inflation, industrial output and exchange rates for India. We cannot reject the null hypothesis for linearity for all series used except for the growth rate of the foreign exchange series and our analysis is based on linear models, ARIMA and bivariate transfer functions and restricted VAR. Forecasting performance is evaluated using the models’ root mean-squared error differences and Theil’s inequality coefficients from recursive origin static, fixed origin dynamic and rolling origin dynamic forecasts. For models based on weekly data, based on RMSEs, we find that the bivariate models improve upon the forecasts of the ARIMA model while for models based on monthly data the ARIMA model has almost always better performance. In choosing between the two bivariate models on the basis of RMSEs, our overall results tend to support the use of a restricted VAR, as this model had the best forecasting performance more frequently than the transfer function model.

N-Acetylcysteine improves mitochondrial function and ameliorates behavioral deficits in the R6/1 mouse model of Huntington's disease

Huntington's disease (HD) is a neurodegenerative disorder, involving psychiatric, cognitive and motor symptoms, caused by a CAG-repeat expansion encoding an extended polyglutamine tract in the huntingtin protein. Oxidative stress and excitotoxicity have previously been implicated in the pathogenesis of HD. We hypothesized that N-acetylcysteine (NAC) may reduce both excitotoxicity and oxidative stress through its actions on glutamate reuptake and antioxidant capacity. The R6/1 transgenic mouse model of HD was used to investigate the effects of NAC on HD pathology. It was found that chronic NAC administration delayed the onset and progression of motor deficits in R6/1 mice, while having an antidepressant-like effect on both R6/1 and wild-type mice. A deficit in the astrocytic glutamate transporter protein, GLT-1, was found in R6/1 mice. However, this deficit was not ameliorated by NAC, implying that the therapeutic effect of NAC is not due to rescue of the GLT-1 deficit and associated glutamate-induced excitotoxicity. Assessment of mitochondrial function in the striatum and cortex revealed that R6/1 mice show reduced mitochondrial respiratory capacity specific to the striatum. This deficit was rescued by chronic treatment with NAC. There was a selective increase in markers of oxidative damage in mitochondria, which was rescued by NAC. In conclusion, NAC is able to delay the onset of motor deficits in the R6/1 model of Huntington's disease and it may do so by ameliorating mitochondrial dysfunction. Thus, NAC shows promise as a potential therapeutic agent in HD. Furthermore, our data suggest that NAC may also have broader antidepressant efficacy.

Laboratory model studies of flushing of trapped salt water from a blocked tidal estuary

Results are presented from a series of laboratory model studies of the flushing of saline water from a partially- or fully-closed estuary. Experiments have been carried out to determine quantitatively the response of the trapped saline volume to fresh water flushing discharges Q for different values of the estuary bed slope α and the density difference (∆ρ)_o between the saline and fresh water. The trapped saline water forms a wedge within the estuary and for maintained steady discharges, flow visualisation and density profile data confirm that its response to the imposition of the freshwater purging flow occurs in two stages, namely (i) an initial phase characterised by intense shear-induced mixing at the nose of the wedge and (ii) a relatively quiescent second phase where the mixing is significantly reduced and the wedge is forced relatively slowly down and along the bed slope. Scalings based upon simple energy balance considerations are shown to be successful in (i) describing the time-dependent wedge behaviour and (ii) quantifying the proportion of input kinetic energy converted into increasing the potential energy of the wedge/river system. Measurements show that the asymptotic value of the energy conversion factor increases with increasing value of the river Froude number Fr_o at small values of Fr_o, thereafter reaching a maximum value and a gradual decrease at the highest values of Fr_o. Dimensional analysis considerations indicate that the normalised, time-dependent wedge position (x_w)³(g')o/q² can be represented empirically by a power-law relationship of the form (x_w)[(g')_o/q²]^1/3 =C [(t)[(g')_o²/q]^1/3]"where the proportionality coefficient C is a function of both Fr_o and the slope angle α and the exponent n has a value of 0.24. Successful attempts are made to relate the model data to existing field observations from a microtidal estuary.

Experiments with multiple, intermittent periodic flushing flows confirm the importance of the starting phase of each flushing event for the time dependent behaviour of the saline wedge after reaching equilibrium in the intervals between such events. For the parameter ranges investigated and for otherwise-identical external conditions, no significant differences are found in the position of the wedge between cases of sequential multiple flushing flows and steady single discharges of the same total duration.

Effect of the toxic milk mutation (tx) on the function and intracellular localization of Wnd, the murine homologue of the Wilson copperATPase

Wilson disease is an autosomal recessive copper transport disorder resulting from defective biliary excretion of copper and subsequent hepatic copper accumulation and liver failure if not treated. The disease is caused by mutations in the ATP7B (WND) gene, which is expressed predominantly in the liver and encodes a copper-transporting P-type ATPase that is structurally and functionally similar to the Menkes protein (MNK), which is defective in the X-linked copper transport disorder Menkes disease. The toxic milk (tx) mouse has a clinical phenotype similar to Wilson disease patients and, recently, the tx mutation within the murine WND homologue (Wnd) of this mouse was identified, establishing it as an animal model for Wilson disease. In this study, cDNA constructs encoding the wild-type (Wnd-wt) and mutant (Wnd-tx) Wilson proteins (Wnd) were generated and expressed in Chinese hamster ovary (CHO) cells. The tx mutation disrupted the copper-induced relocalization of Wnd in CHO cells and abrogated Wnd-mediated copper resistance of transfected CHO cells. In addition, co-localization experiments demonstrated that while Wnd and MNK are located in the trans-Golgi network in basal copper conditions, with elevated copper, these proteins are sorted to different destinations within the same cell. Ultrastructural studies showed that with elevated copper levels, Wnd accumulated in large multi-vesicular structures resembling late endosomes that may represent a novel compartment for copper transport. The data presented provide further support for a relationship between copper transport activity and the copper-induced relocalization response of mammalian copper ATPases, and an explanation at a molecular level for the observed phenotype of tx mice

The zebrafish as a model system for human disease

The zebrafish (Danio rerio) has been widely utilised for the study of developmental biology, which has lead to the evolution of sophisticated cellular and molecular approaches. More recently, the rapid progress of various zebrafish genomic infrastructure initiatives is facilitating the development of zebrafish models of human disease. This review aims to describe several representative examples of how the zebrafish can be successfully used to identify novel genes and assign gene function, providing invaluable clues to human pathophysiology.

Knowledge extraction from a mixed transfer function artificial neural network

One of the main problems with Artificial Neural Networks (ANNs) is that their results are not intuitively clear. For example, commonly used hidden neurons with sigmoid activation function can approximate any continuous function, including linear functions, but the coefficients (weights) of this approximation are rather meaningless. To address this problem, current paper presents a novel kind of a neural network that uses transfer functions of various complexities in contrast to mono-transfer functions used in sigmoid and hyperbolic tangent networks. The presence of transfer functions of various complexities in a Mixed Transfer Functions Artificial Neural Network (MTFANN) allow easy conversion of the full model into user-friendly equation format (similar to that of linear regression) without any pruning or simplification of the model. At the same time, MTFANN maintains similar generalization ability to mono-transfer function networks in a global optimization context. The performance and knowledge extraction of MTFANN were evaluated on a realistic simulation of the Puma 560 robot arm and compared to sigmoid, hyperbolic tangent, linear and sinusoidal networks.

Improving an inverse model of sheet metal forming by neural network based regression

An inverse model for a sheet meta l forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such as finite element analysis. Formulating the problem as a classification problem makes it possible to use well established classification algorithms, such as decision trees. Classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand, when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations compared with classification between the output of the model and the corresponding class. Such formulation makes it possible to use well known regression algorithms, such as neural networks. In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes, classification mode and a function estimation mode, to investigate the advantage of re-formulating the problem as a function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameter recognition than a linear model.

An innovative spam filtering model based on support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, an innovative and intelligent spam filtering model has been proposed based on support vector machine (SVM). This model combines both linear and nonlinear SVM techniques where linear SVM performs better for text based spam classification that share similar characteristics. The proposed model considers both text and image based email messages for classification by selecting an appropriate kernel function for information transformation.

Analysis of work hardening and recrystallization during the hot working of steel using a statistically based internal variable model

A mathematical model has been developed which describes the hot deformation and recrystallization behavior of austenite using a single internal variable: dislocation density. The dislocation density is incorporated into equations describing the rate of recovery and recrystallization. In each case no distinction is made between static and dynamic events, and the model is able to simulate multideformation processes. The model is statistically based and tracks individual populations of the dislocation density during the work-hardening and softening phases. After tuning using available data the model gave an accurate prediction of the stress–strain behavior and the static recrystallization kinetics for C–Mn steels. The model correctly predicted the sensitivity of the post deformation recrystallization behavior to process variables such as strain, strain rate and temperature, even though data for this were not explicitly incorporated in the tuning data set. In particular, the post dynamic recrystallization (generally termed metadynamic recrystallization) was shown to be largely independent of strain and temperature, but a strong function of strain rate, as observed in published experimental work.

A strategic approach to target marketing-choosing the preferred generic customer : a model of shareholder value performance

In recent years the contribution of the marketing function has changed and interest now centres on its contribution to a firm’s financial performance. The Marketing Science Institute in the USA has stated that it is the number one marketing issue facing corporate America. The Australian Marketing Institute is promoting a set of marketing metrics that will help Australian firms measure the function’s contribution to shareholder value creation. Much of the literature relates notions such as customer satisfaction and other marketing activities with a firm’s profit. A missing link appears to be the choice firms make in terms of which customer groups to target and the resultant impact on shareholder value performance. The generic customer groups comprise: existing customers, former customers and prospects. A review of the literature reveals that marketing costs and benefits vary across these groups. The challenge for management is to determine which group represents the best target and to allocate scarce marketing resources accordingly. The task is made even more challenging because the economic value of members within each group also varies and some product lines may be unprofitable and therefore, may not be worth pursuing. To generate superior shareholder value it may not simply be the case of acquiring the maximum number of new customers from any source but to find the appropriate mix of the generic customer groups and manage the individual customer relationships accordingly. This paper seeks to firstly summarise and review the recent literature on marketing and its relationship to shareholder value and secondly to propose a model for allocating marketing resources across generic customer groups in order to generate improved shareholder value performance. Importantly, the model not only covers increasing business with customers but also shedding customers or shedding the extent of business conducted with customers as means of generating shareholder value.

Fractional polynomials and model selection in generalized estimating equations analysis, with an application to a longitudinal epidemiologic study in Australia

In epidemiologic studies, researchers often need to establish a nonlinear exposure-response relation between a continuous risk factor and a health outcome. Furthermore, periodic interviews are often conducted to take repeated measurements from an individual. The authors proposed to use fractional polynomial models to jointly analyze the effects of 2 continuous risk factors on a health outcome. This method was applied to an analysis of the effects of age and cumulative fluoride exposure on forced vital capacity in a longitudinal study of lung function carried out among aluminum workers in Australia (1995-2003). Generalized estimating equations and the quasi-likelihood under the independence model criterion were used. The authors found that the second-degree fractional polynomial models for age and fluoride fitted the data best. The best model for age was robust across different models for fluoride, and the best model for fluoride was also robust. No evidence was found to suggest that the effects of smoking and cumulative fluoride exposure on change in forced vital capacity over time were significant. The trend 1 model, which included the unexposed persons in the analysis of trend in forced vital capacity over tertiles of fluoride exposure, did not fit the data well, and caution should be exercised when this method is used.

An econometric model of the determinants of private investment and a CGE model of the impact of democracy on investment and economic growth in Fiji

Purpose – The purpose of this paper is to construct an econometric model of the determinants of private investment with a particular focus on the impact of democracy on investment.

Design/methodology/approach – The first step was to econometrically derive the long-run elasticities; then to modify the Fiji computable general equilibrium (CGE) model to incorporate the investment function. Also the econometrically derived long run elasticities in the CGE model were used.

Findings – It was found that democracy has a positive and statistically significant impact on private investment in Fiji. The paper's simulation of Fiji becoming a fully democratic country on investment and other macroeconomic fundamentals, based on a CGE model, reveals that real gross domestic product and real national welfare increase by around 0.01 and 0.05 per cent, respectively; government savings and revenue performance improves; there is a trade balance surplus; and both private consumption and disposable income increase by around 0.05 and 0.12 per cent, respectively.

Originality/value – This is the first study that uses a CGE model to examine the impact of democracy, via investment, on other macroeconomic fundaments. No other study is known to have modelled democracy in a CGE framework.