Macroeconomic policy in an output-expenditure model

This paper proposes an alternative framework for examining the international macroeconomic impact of domestic monetary and fiscal policies and focuses on the distinction between national spending and national production and the reactive behavior of foreign investors to changing external account balances. It demonstrates that under a floating exchange rate regime, monetary and fiscal policies can affect aggregate expenditure and output quite differently, with important implications for the behavior of the exchange rate, the current account balance, and national income in the short run, as well as the economy's price level in the long run. In particular, this paper predicts that expansionary monetary and fiscal policies tend to depreciate the currency and only temporarily raise gross domestic product and the current account surplus, although permanently raise the domestic price level. This is a revised version of a paper presented at the Forty-Ninth International Atlantic Economic Conference, March 14–21, 2000, Munich, Germany.

An orthotopic xenograft model of human nonseminomatous germ cell tumour

We have established the first example of an orthotopic xenograft model of human nonseminomatous germ cell tumour (NSGCT). This reproducible model exhibits many clinically relevant features including metastases to the retroperitoneal lymph nodes and lungs, making it an ideal tool for research into the development and progression of testicular germ cell tumours.

Habitat complexity, spatial interference, and "minimum risk distribution": a framework for population stability

In the past century, the debate over whether or not density-dependent factors regulate populations has generally focused on changes in mean population density, ignoring the spatial variance around the mean as unimportant noise. In an attempt to provide a different framework for understanding population dynamics based on individual fitness, this paper discusses the crucial role of spatial variability itself on the stability of insect populations. The advantages of this method are the following: (1) it is founded on evolutionary principles rather than post hoc assumptions; (2) it erects hypotheses that can be tested; and (3) it links disparate ecological schools, including spatial dynamics, behavioral ecology, preference-performance, and plant apparency into an overall framework. At the core of this framework, habitat complexity governs insect spatial variance. which in turn determines population stability. First, the minimum risk distribution (MRD) is defined as the spatial distribution of individuals that results in the minimum number of premature deaths in a population given the distribution of mortality risk in the habitat (and, therefore, leading to maximized population growth). The greater the divergence of actual spatial patterns of individuals from the MRD, the greater the reduction of population growth and size from high, unstable levels. Then, based on extensive data from 29 populations of the processionary caterpillar, Ochrogaster lunifer, four steps are used to test the effect of habitat interference on population growth rates. (1) The costs (increasing the risk of scramble competition) and benefits (decreasing the risk of inverse density-dependent predation) of egg and larval aggregation are quantified. (2) These costs and benefits, along with the distribution of resources, are used to construct the MRD for each habitat. (3) The MRD is used as a benchmark against which the actual spatial pattern of individuals is compared. The degree of divergence of the actual spatial pattern from the MRD is quantified for each of the 29 habitats. (4) Finally, indices of habitat complexity are used to provide highly accurate predictions of spatial divergence from the MRD, showing that habitat interference reduces population growth rates from high, unstable levels. The reason for the divergence appears to be that high levels of background vegetation (vegetation other than host plants) interfere with female host-searching behavior. This leads to a spatial distribution of egg batches with high mortality risk, and therefore lower population growth. Knowledge of the MRD in other species should be a highly effective means of predicting trends in population dynamics. Species with high divergence between their actual spatial distribution and their MRD may display relatively stable dynamics at low population levels. In contrast, species with low divergence should experience high levels of intragenerational population growth leading to frequent habitat-wide outbreaks and unstable dynamics in the long term. Six hypotheses, erected under the framework of spatial interference, are discussed, and future tests are suggested.

An improved survival model of hypoxia/ischaemia in the piglet suitable for neuroprotection studies

The purpose of this study, was to develop a newborn piglet model of hypoxia/ischaemia which would better emulate the clinical situation in the asphyxiated human neonate and produce a consistent degree of histopathological injury following the insult. One-day-old piglets (n = 18) were anaesthetised with a mixture of propofol (10 mg/kg/h) and alfentinal (5,5.5 mug/kg/h) i.v. The piglets were intubated and ventilated. Physiological variables were monitored continuously. Hypoxia was induced by decreasing the inspired oxygen (FiO(2)) to 3-4% and adjusting FiO(2) to maintain the cerebral function monitor peak amplitude at less than or equal to5 muV. The duration of the mild insult was 20, min while the severe insult was 30 min which included 10 min where the blood pressure was allowed to fall below 70% of baseline. Control piglets (n=4 of 18) were subjected to the same protocol except for the hypoxic/ischaemic insult. The piglets were allowed to recover from anaesthesia then euthanased 72 It after the insult. The brains were perfusion-fixed, removed and embedded in paraffin. Coronal sections were stained by haematoxylin/eosin. A blinded observer examined the frontal and parietal cortex, hippocampus, basal ganglia, thalamus and cerebellum for the degree of damage. The total mean histology score for the five areas of the brain for the severe insult was 15.6 +/-4.4 (mean +/-S.D., n=7), whereas no damage was seen in either the mild insult (n=4) or control groups. This 'severe damage' model produces a consistent level of damage and will prove useful for examining potential neuroprotective therapies in the neonatal brain. (C) 2001 Elsevier Science BY. All rights reserved.

Increasing profits and reducing risks in crop production using participatory systems simulation approaches

The development of cropping systems simulation capabilities world-wide combined with easy access to powerful computing has resulted in a plethora of agricultural models and consequently, model applications. Nonetheless, the scientific credibility of such applications and their relevance to farming practice is still being questioned. Our objective in this paper is to highlight some of the model applications from which benefits for farmers were or could be obtained via changed agricultural practice or policy. Changed on-farm practice due to the direct contribution of modelling, while keenly sought after, may in some cases be less achievable than a contribution via agricultural policies. This paper is intended to give some guidance for future model applications. It is not a comprehensive review of model applications, nor is it intended to discuss modelling in the context of social science or extension policy. Rather, we take snapshots around the globe to 'take stock' and to demonstrate that well-defined financial and environmental benefits can be obtained on-farm from the use of models. We highlight the importance of 'relevance' and hence the importance of true partnerships between all stakeholders (farmer, scientists, advisers) for the successful development and adoption of simulation approaches. Specifically, we address some key points that are essential for successful model applications such as: (1) issues to be addressed must be neither trivial nor obvious; (2) a modelling approach must reduce complexity rather than proliferate choices in order to aid the decision-making process (3) the cropping systems must be sufficiently flexible to allow management interventions based on insights gained from models. The pro and cons of normative approaches (e.g. decision support software that can reach a wide audience quickly but are often poorly contextualized for any individual client) versus model applications within the context of an individual client's situation will also be discussed. We suggest that a tandem approach is necessary whereby the latter is used in the early stages of model application for confidence building amongst client groups. This paper focuses on five specific regions that differ fundamentally in terms of environment and socio-economic structure and hence in their requirements for successful model applications. Specifically, we will give examples from Australia and South America (high climatic variability, large areas, low input, technologically advanced); Africa (high climatic variability, small areas, low input, subsistence agriculture); India (high climatic variability, small areas, medium level inputs, technologically progressing; and Europe (relatively low climatic variability, small areas, high input, technologically advanced). The contrast between Australia and Europe will further demonstrate how successful model applications are strongly influenced by the policy framework within which producers operate. We suggest that this might eventually lead to better adoption of fully integrated systems approaches and result in the development of resilient farming systems that are in tune with current climatic conditions and are adaptable to biophysical and socioeconomic variability and change. (C) 2001 Elsevier Science Ltd. All rights reserved.

An aggregate sales model for consumer durables incorporating a time-varying mean replacement age

Forecasting category or industry sales is a vital component of a company's planning and control activities. Sales for most mature durable product categories are dominated by replacement purchases. Previous sales models which explicitly incorporate a component of sales due to replacement assume there is an age distribution for replacements of existing units which remains constant over time. However, there is evidence that changes in factors such as product reliability/durability, price, repair costs, scrapping values, styling and economic conditions will result in changes in the mean replacement age of units. This paper develops a model for such time-varying replacement behaviour and empirically tests it in the Australian automotive industry. Both longitudinal census data and the empirical analysis of the replacement sales model confirm that there has been a substantial increase in the average aggregate replacement age for motor vehicles over the past 20 years. Further, much of this variation could be explained by real price increases and a linear temporal trend. Consequently, the time-varying model significantly outperformed previous models both in terms of fitting and forecasting the sales data. Copyright (C) 2001 John Wiley & Sons, Ltd.

Effect of Fusobacterium nucleatum on the T and B cell responses to Porphyromonas gingivalis in a mouse model

T cell cytokine profiles and specific serum antibody levels in five groups of BALB/c mice immunized with saline alone, viable Fusobacterium nucleatum ATCC 25586, viable Porphyromonas gingivalis ATCC 33277, F. nucleatum followed by P. gingivalis and P. gingivalis followed by F nucleatum were determined. Splenic CD4 and CD8 cells were examined for intracytoplasmic interleukin (IL)-4, interferon (IFN)-gamma and IL-10 by dual colour flow cytometry and the levels of serum anti-F. nucleatum and anti-P. gingivalis antibodies determined by an ELISA. Both Th1 and Th2 responses were demonstrated by all groups, and while there were slightly lower percentages of cytokine positive T cells in mice injected with F. nucleatum alone compared with the other groups immunized with bacteria., F nucleatum had no effect on the T cell production of cytokines induced by P gingivalis in the two groups immunized with both organisms. However, the percentages of cytokine positive CD8 cells were generally significantly higher than those of the CD4 cells. Mice immunized with F nucleatum alone had high levels of serum anti-E nucleatum antibodies with very low levels of P. gingivalis antibodies, whereas mice injected with P gingivalis alone produced anti-P. gingivalis antibodies predominantly. Although the levels of anti-E nucleatum antibodies in mice injected with E nucleatum followed by P. gingivalis were the same as in mice immunized with F nucleatum alone, antibody levels to P. gingivalis were very low. In contrast, mice injected with P. gingivalis followed by F nucleatum produced equal levels of both anti-P. gingivalis and anti-F nucleatum antibodies, although at lower levels than the other three groups immunized with bacteria, respectively. Anti-Actinobacillus actitiomycetemcomitans, Bacteroides forsythus and Prevotella intermedia serum antibody levels were also determined and found to be negligible. In conclusion, F nucleatum immunization does not affect the splenic T cell cytokine response to P. gingivalis. However, F nucleatum immunization prior to that of P. gingivalis almost completely inhibited the production of anti-P gingivalis antibodies while P. gingivalis injection before F. nucleatum demonstrated a partial inhibitory effect by P. gingivalis on antibody production to F. nucleatum. The significance of these results with respect to human periodontal disease is difficult to determine. However, they may explain in part differing responses to P. gingivalis in different individuals who may or may not have had prior exposure to F. nucleatum. Finally, the results suggested that P. gingivalis and F. nucleatum do not induce the production of cross-reactive antibodies to other oral microorganisms.

The IWA Anaerobic Digestion Model No 1 (ADM1)

The IWA Anaerobic Digestion Modelling Task Group was established in 1997 at the 8th World Congress on,Anaerobic Digestion (Sendai, Japan) with the goal of developing a generalised anaerobic digestion model. The structured model includes multiple steps describing biochemical as well as physicochemical processes. The biochemical steps include disintegration from homogeneous particulates to carbohydrates, proteins and lipids; extracellular hydrolysis of these particulate substrates to sugars, amino acids, and long chain fatty acids (LCFA), respectively; acidogenesis from sugars and amino acids to volatile fatty acids (VFAs) and hydrogen; acetogenesis of LCFA and VFAs to acetate; and separate methanogenesis steps from acetate and hydrogen/CO2. The physico-chemical equations describe ion association and dissociation, and gas-liquid transfer. Implemented as a differential and algebraic equation (DAE) set, there are 26 dynamic state concentration variables, and 8 implicit algebraic variables per reactor vessel or element. Implemented as differential equations (DE) only, there are 32 dynamic concentration state variables.

Determining when the absolute state complexity of a Hermitian code achieves its DLP bound

Let g be the genus of the Hermitian function field H/F(q)2 and let C-L(D,mQ(infinity)) be a typical Hermitian code of length n. In [Des. Codes Cryptogr., to appear], we determined the dimension/length profile (DLP) lower bound on the state complexity of C-L(D,mQ(infinity)). Here we determine when this lower bound is tight and when it is not. For m less than or equal to n-2/2 or m greater than or equal to n-2/2 + 2g, the DLP lower bounds reach Wolf's upper bound on state complexity and thus are trivially tight. We begin by showing that for about half of the remaining values of m the DLP bounds cannot be tight. In these cases, we give a lower bound on the absolute state complexity of C-L(D,mQ(infinity)), which improves the DLP lower bound. Next we give a good coordinate order for C-L(D,mQ(infinity)). With this good order, the state complexity of C-L(D,mQ(infinity)) achieves its DLP bound (whenever this is possible). This coordinate order also provides an upper bound on the absolute state complexity of C-L(D,mQ(infinity)) (for those values of m for which the DLP bounds cannot be tight). Our bounds on absolute state complexity do not meet for some of these values of m, and this leaves open the question whether our coordinate order is best possible in these cases. A straightforward application of these results is that if C-L(D,mQ(infinity)) is self-dual, then its state complexity (with respect to the lexicographic coordinate order) achieves its DLP bound of n /2 - q(2)/4, and, in particular, so does its absolute state complexity.

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.

A mixture model-based approach to the clustering of microarray expression data

Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.

Reconciling value estimates from the discounted cash flow model and the residual income model

This paper examines why practitioners and researchers get different estimates of equity value when they use a discounted cash flow (CF) model versus a residual income (RI) model. Both models are derived from the same underlying assumption -- that price is the present value of expected future net dividends discounted at the cost of equity capital -- but in practice and in research they frequently yield different estimates. We argue that the research literature devoted to comparing the accuracy of these two models is misguided; properly implemented, both models yield identical valuations for all firms in all years. We identify how prior research has applied inconsistent assumptions to the two models and show how these seemingly small errors cause surprisingly large differences in the value estimates. [ABSTRACT FROM AUTHOR]

Developing a meta model for the Bunge-Wand-Weber ontological constructs

At the core of the analysis task in the development process is information systems requirements modelling, Modelling of requirements has been occurring for many years and the techniques used have progressed from flowcharting through data flow diagrams and entity-relationship diagrams to object-oriented schemas today. Unfortunately, researchers have been able to give little theoretical guidance only to practitioners on which techniques to use and when. In an attempt to address this situation, Wand and Weber have developed a series of models based on the ontological theory of Mario Bunge-the Bunge-Wand-Weber (BWW) models. Two particular criticisms of the models have persisted however-the understandability of the constructs in the BWW models and the difficulty in applying the models to a modelling technique. This paper addresses these issues by presenting a meta model of the BWW constructs using a meta language that is familiar to many IS professionals, more specific than plain English text, but easier to understand than the set-theoretic language of the original BWW models. Such a meta model also facilitates the application of the BWW theory to other modelling techniques that have similar meta models defined. Moreover, this approach supports the identification of patterns of constructs that might be common across meta models for modelling techniques. Such findings are useful in extending and refining the BWW theory. (C) 2002 Elsevier Science Ltd. All rights reserved.