A Bayesian hurdle model for analysis of an insect resistance monitoring database

Motivated by the analysis of the Australian Grain Insect Resistance Database (AGIRD), we develop a Bayesian hurdle modelling approach to assess trends in strong resistance of stored grain insects to phosphine over time. The binary response variable from AGIRD indicating presence or absence of strong resistance is characterized by a majority of absence observations and the hurdle model is a two step approach that is useful when analyzing such a binary response dataset. The proposed hurdle model utilizes Bayesian classification trees to firstly identify covariates and covariate levels pertaining to possible presence or absence of strong resistance. Secondly, generalized additive models (GAMs) with spike and slab priors for variable selection are fitted to the subset of the dataset identified from the Bayesian classification tree indicating possibility of presence of strong resistance. From the GAM we assess trends, biosecurity issues and site specific variables influencing the presence of strong resistance using a variable selection approach. The proposed Bayesian hurdle model is compared to its frequentist counterpart, and also to a naive Bayesian approach which fits a GAM to the entire dataset. The Bayesian hurdle model has the benefit of providing a set of good trees for use in the first step and appears to provide enough flexibility to represent the influence of variables on strong resistance compared to the frequentist model, but also captures the subtle changes in the trend that are missed by the frequentist and naive Bayesian models. © 2014 Springer Science+Business Media New York.

Generalised growth models for aquatic species with an application to blacklip abalone (Haliotis rubra)

This paper presents a maximum likelihood method for estimating growth parameters for an aquatic species that incorporates growth covariates, and takes into consideration multiple tag-recapture data. Individual variability in asymptotic length, age-at-tagging, and measurement error are also considered in the model structure. Using distribution theory, the log-likelihood function is derived under a generalised framework for the von Bertalanffy and Gompertz growth models. Due to the generality of the derivation, covariate effects can be included for both models with seasonality and tagging effects investigated. Method robustness is established via comparison with the Fabens, improved Fabens, James and a non-linear mixed-effects growth models, with the maximum likelihood method performing the best. The method is illustrated further with an application to blacklip abalone (Haliotis rubra) for which a strong growth-retarding tagging effect that persisted for several months was detected. (C) 2013 Elsevier B.V. All rights reserved.

Bond graph model of doubly fed three phase induction motor using the Axis Rotator element for frame transformation

Induction motor is a typical member of a multi-domain, non-linear, high order dynamic system. For speed control a three phase induction motor is modelled as a d–q model where linearity is assumed and non-idealities are ignored. Approximation of the physical characteristic gives a simulated behaviour away from the natural behaviour. This paper proposes a bond graph model of an induction motor that can incorporate the non-linearities and non-idealities thereby resembling the physical system more closely. The model is validated by applying the linearity and idealities constraints which shows that the conventional ‘abc’ model is a special case of the proposed generalised model.

Generalised regression estimation for domain class frequencies

This study examines the properties of Generalised Regression (GREG) estimators for domain class frequencies and proportions. The family of GREG estimators forms the class of design-based model-assisted estimators. All GREG estimators utilise auxiliary information via modelling. The classic GREG estimator with a linear fixed effects assisting model (GREG-lin) is one example. But when estimating class frequencies, the study variable is binary or polytomous. Therefore logistic-type assisting models (e.g. logistic or probit model) should be preferred over the linear one. However, other GREG estimators than GREG-lin are rarely used, and knowledge about their properties is limited. This study examines the properties of L-GREG estimators, which are GREG estimators with fixed-effects logistic-type models. Three research questions are addressed. First, I study whether and when L-GREG estimators are more accurate than GREG-lin. Theoretical results and Monte Carlo experiments which cover both equal and unequal probability sampling designs and a wide variety of model formulations show that in standard situations, the difference between L-GREG and GREG-lin is small. But in the case of a strong assisting model, two interesting situations arise: if the domain sample size is reasonably large, L-GREG is more accurate than GREG-lin, and if the domain sample size is very small, estimation of assisting model parameters may be inaccurate, resulting in bias for L-GREG. Second, I study variance estimation for the L-GREG estimators. The standard variance estimator (S) for all GREG estimators resembles the Sen-Yates-Grundy variance estimator, but it is a double sum of prediction errors, not of the observed values of the study variable. Monte Carlo experiments show that S underestimates the variance of L-GREG especially if the domain sample size is minor, or if the assisting model is strong. Third, since the standard variance estimator S often fails for the L-GREG estimators, I propose a new augmented variance estimator (A). The difference between S and the new estimator A is that the latter takes into account the difference between the sample fit model and the census fit model. In Monte Carlo experiments, the new estimator A outperformed the standard estimator S in terms of bias, root mean square error and coverage rate. Thus the new estimator provides a good alternative to the standard estimator.

A generalised cole-hopf transformation for nonlinear parabolic and hyperbolic equations

The Cole-Hopf transformation has been generalized to generate a large class of nonlinear parabolic and hyperbolic equations which are exactly linearizable. These include model equations of exchange processes and turbulence. The methods to solve the corresponding linear equations have also been indicated.La transformation de Cole et de Hopf a été généralisée en vue d'engendrer une classe d'équations nonlinéaires paraboliques et hyperboliques qui peuvent être rendues linéaires de façon exacte. Elles comprennent des équations modèles de procédés d'échange et de turbulence. Les méthodes pour résoudre les équations linéaires correspondantes ont également été indiquées.

Non-Fermi liquid behavior in a mean-field study of the t-J model: Role of zero-point spin fluctuations

We present analytic results to show that the Schwinger-boson hole-fermion mean-field state exhibits non-Fermi liquid behavior due to spin-charge separation. The physical electron Green's function consists of three additive components. (a) A Fermi-liquid component associated with the bose condensate. (b) A non-Fermi liquid component which has a logarithmic peak and a long tail that gives rise to a linear density of states that is symmetric about the Fermi level and a momentum distribution function with a logarithmic discontinuity at the Fermi surface. (c) A second non-Fermi liquid component associated with the thermal bosons which leads to a constant density of states. It is shown that zero-point fluctuations associated with the spin-degrees of freedom are responsible for the logarithmic instabilities and the restoration of particle-hole symmetry close to the Fermi surface.

A mixture model approach for formant tracking and the robustness of student's-t distribution

We address the problem of robust formant tracking in continuous speech in the presence of additive noise. We propose a new approach based on mixture modeling of the formant contours. Our approach consists of two main steps: (i) Computation of a pyknogram based on multiband amplitude-modulation/frequency-modulation (AM/FM) decomposition of the input speech; and (ii) Statistical modeling of the pyknogram using mixture models. We experiment with both Gaussian mixture model (GMM) and Student's-t mixture model (tMM) and show that the latter is robust with respect to handling outliers in the pyknogram data, parameter selection, accuracy, and smoothness of the estimated formant contours. Experimental results on simulated data as well as noisy speech data show that the proposed tMM-based approach is also robust to additive noise. We present performance comparisons with a recently developed adaptive filterbank technique proposed in the literature and the classical Burg's spectral estimator technique, which show that the proposed technique is more robust to noise.

Proposal for generalised supersymmetry Les Houches Accord for see-saw models and PDG numbering scheme

The SUSY Les Houches Accord (SLHA) 2 extended the first SLHA to include various generalisations of the Minimal Supersymmetric Standard Model (MSSM) as well as its simplest next-to-minimal version. Here, we propose further extensions to it, to include the most general and well-established see-saw descriptions (types I/II/III, inverse, and linear) in both an effective and a simple gauged extension of the MSSM framework. In addition, we generalise the PDG numbering scheme to reflect the properties of the particles. (c) 2012 Elsevier B.V. All rights reserved.

Zoledronic acid in combination with alfacalcidol has additive effects on trabecular microarchitecture and mechanical properties in osteopenic ovariectomized rats

We conducted the present study to investigate the therapeutic effects of the antiresorptive agent zoledronic acid (ZOL), alone and in combination with alfacalcidol (ALF), in a rat model of postmenopausal osteoporosis. Female Wistar rats were ovariectomized (OVX) or sham-operated at 3 months of age. Twelve weeks post surgery, rats were randomized into six groups: (1) sham + vehicle, (2) OVX + vehicle, (3) OVX + ZOL (100 mu g/kg, i.v. single dose), (4) OVX + ZOL (50 mu g/kg, i.v. single dose), (5) OVX + ALF (0.5 mu g/kg, oral gauge daily) and (6) OVX + ZOL (50 mu g/kg, i.v. single dose) + ALF (0.5 mu g/kg, oral gauge daily) for 12 weeks. After treatment, we evaluated the mechanical properties of the lumbar vertebra and femoral mid-shaft. Femurs were also tested for bone density, porosity and trabecular micro-architecture. Biochemical markers in serum and urine were also determined. With respect to improvement in the mechanical strength of the lumbar spine and the femoral mid-shaft, the combination treatment of ZOL and ALF was more effective than each administered as a monotherapy. Moreover, combination therapy using ZOL and ALF preserved the trabecular micro-architecture and cortical bone porosity. Furthermore, the combination treatment of ZOL and ALF corrected the decrease in serum calcium and increase in serum alkaline phosphatase and the tartarate-resistant acid phosphatase level better than single-drug therapy using ZOL or ALF in OVX rats. In addition, the combination treatment of ZOL and ALF corrected the increase in urine calcium, phosphorous and creatinine levels better than single-drug therapy using ZOL or ALF in OVX rats. These data suggest that the combination treatment of ZOL and ALF has a therapeutic advantage over each monotherapy for the treatment of osteoporosis.

Additive effect of zoledronic acid and alfacalcidol in the treatment of disuse osteoporosis in rats

Objectives: Disuse by bed rest, limb immobilization or space flight causes rapid bone loss. We conducted the present study to investigate the therapeutic effects of zoledronic acid (ZOL), alone and in combination with alfacalcidol (ALP) in a rat model of disuse osteoporosis. Methods: In the present study, 3-month-old male Wistar rats had their right hind-limb immobilized (RHLI) for 10 weeks to induce osteopenia, then were divided into four groups: 1 - RHLI positive control; 2 - RHLI plus ZOL (50 mu g/kg, i.v. single dose); 3 - RHLI plus ALP (0.5 mu g/kg, oral gauge daily); 4- RHLI plus ALP (0.5 mu g/kg, oral gauge daily) plus ZOL (50 mu g/kg, i.v. single dose) for another 10 weeks. One group of non-immobilized rats was used as negative control. At the end of the treatment, the femurs were removed and tested for bone porosity, bone mechanical properties, and bone dry and ash weight. Results: Combination therapy with ZOL plus ALP was more effective in decreasing bone porosity than each drug administered as monotherapy in RHLI rats. With respect to improvement in the mechanical strength of the femoral mid-shaft, the combination treatment of ZOL plus ALP was more effective than each drug administered as a monotherapy. Moreover, combination therapy using ZOL plus ALF was more effective in improving dry bone and ash weight, than single-drug therapy using ZOL or ALP in RHLI rats. Conclusions: These data suggest that combination therapy with ZOL plus ALP represents a potentially useful therapeutic option for the treatment of disuse osteoporosis. (C) 2014 Elsevier Editora Ltda. All rights reserved.

Effect of combined treatment with zoledronic acid and propranolol on mechanical strength in an rat model of disuse osteoporosis

Objectives: A model that uses right hind-limb unloading of rats is used to study the consequences of skeletal unloading during various conditions like space flights and prolonged bed rest in elderly. This study was aimed to investigate the additive effects of antiresorptive agent zoledronic acid (ZOL), alone and in combination with propranolol (PRO) in a rat model of disuse osteoporosis. Methods: In the present study, 3-month-old male Wistar rats had their right hind-limb immobilized (RHLI) for 10 weeks to induce osteopenia, then were randomized into four groups: 1-RHLI positive control, 2-RHLI plus ZOL (50 mu g/kg, i.v. single dose), 3-RHLI plus PRO (0.1 mg/kg, s.c. 5 days per week), 4-RHLI plus PRO (0.1 mg/kg, s.c. 5 days per week) plus ZOL (50 mu g/kg, i.v. single dose) for another 10 weeks. One group of non-immobilized rats was used as negative control. At the end of treatment, the femurs were removed and tested for bone porosity, bone mechanical properties, and bone dry and ash weight. Results: With respect to improvement in the mechanical strength of the femoral mid-shaft, the combination treatment with ZOL plus PRO was more effective than ZOL or PRO monotherapy. Moreover, combination therapy using ZOL plus PRO was more effective in improving dry bone weight and preserved the cortical bone porosity better than monotherapy using ZOL or PRO in right hind-limb immobilized rats. Conclusions: These data suggest that this combined treatment with ZOL plus PRO should be recommended for the treatment of disuse osteoporosis. (C) 2014 Elsevier Editora Ltda. All rights reserved.

Age-dependent decrease in glutamine synthetase expression in the hippocampal astroglia of the triple transgenic Alzheimer's disease mouse model : mechanism for deficient glutamatergic transmission?

Astrocytes are fundamental for brain homeostasis and the progression and outcome of many neuropathologies including Alzheimer's disease (AD). In the triple transgenic mouse model of AD (3xTg-AD) generalised hippocampal astroglia atrophy precedes a restricted and specific beta-amyloid (A beta) plaque-related astrogliosis. Astrocytes are critical for CNS glutamatergic transmission being the principal elements of glutamate homeostasis through maintaining its synthesis, uptake and turnover via glutamate-glutamine shuttle. Glutamine synthetase (GS), which is specifically expressed in astrocytes, forms glutamine by an ATP-dependent amination of glutamate. Here, we report changes in GS astrocytic expression in two major cognitive areas of the hippocampus (the dentate gyrus, DG and the CA1) in 3xTg-AD animals aged between 9 and 18 months. We found a significant reduction in Nv (number of cell/mm(3)) of GS immunoreactive (GS-IR) astrocytes starting from 12 months (28.59%) of age in the DG, and sustained at 18 months (31.65%). CA1 decrease of GS-positive astrocytes Nv (33.26%) occurs at 18 months. This Nv reduction of GSIR astrocytes is paralleled by a decrease in overall GS expression (determined by its optical density) that becomes significant at 18 months (21.61% and 19.68% in DG and CA1, respectively). GS-IR Nv changes are directly associated with the presence of A beta deposits showing a decrease of 47.92% as opposed to 23.47% in areas free of A beta. These changes in GS containing astrocytes and GS-immunoreactivity indicate AD-related impairments of glutamate homeostatic system, at the advanced and late stages of the disease, which may affect the efficacy of glutamatergic transmission in the diseased brain that may contribute to the cognitive deficiency.

Generalised Wishart Processes

We introduce a stochastic process with Wishart marginals: the generalised Wishart process (GWP). It is a collection of positive semi-definite random matrices indexed by any arbitrary dependent variable. We use it to model dynamic (e.g. time varying) covariance matrices. Unlike existing models, it can capture a diverse class of covariance structures, it can easily handle missing data, the dependent variable can readily include covariates other than time, and it scales well with dimension; there is no need for free parameters, and optional parameters are easy to interpret. We describe how to construct the GWP, introduce general procedures for inference and predictions, and show that it outperforms its main competitor, multivariate GARCH, even on financial data that especially suits GARCH. We also show how to predict the mean of a multivariate process while accounting for dynamic correlations.

Additive Gaussian processes

We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-exponential kernels. Hyperparameter learning in this model can be seen as Bayesian Hierarchical Kernel Learning (HKL). We introduce an expressive but tractable parameterization of the kernel function, which allows efficient evaluation of all input interaction terms, whose number is exponential in the input dimension. The additional structure discoverable by this model results in increased interpretability, as well as state-of-the-art predictive power in regression tasks.

A four-phase confocal elliptical cylinder model for predicting the effective thermal conductivity of coated fibre composites

A four-phase confocal elliptical cylinder model is proposed from which a generalised self-consistent method is developed for predicting the thermal conductivity of coated fibre reinforced composites. The method can account for the influence of the fibre section shape ratio on conductivity, and the physical reasonableness of the model is demonstrated by using the fibre distribution function. An exact solution is obtained for thermal conductivity by applying conformal mapping and Laurent series expansion techniques of the analytic function. The solution to the three-phase confocal elliptical model, which simulates composites with idealised fibre-matrix interfaces, is arrived at as the degenerated case. A comparison with other available micromechanics methods, Hashin and Shtrikman's bounds and experimental data shows that the present method provides convergent and reasonable results for a full range of variations in fibre section shapes and for a complete spectrum of the fibre volume fraction. Numerical results show the dependence of the effective conductivities of composites on the aspect ratio of coated fibres and demonstrate that a coating is effective in enhancing the thermal transport property of a composite. The present solutions are helpful to analysis and design of composites.