Natural products as alternative treatments for metabolic bone disorders and for maintenance of bone health

Bone metabolism involves a complex balance between the deposition of matrix and mineralization and resorption. There is now good evidence that dietary components and herbal products can influence these processes, particularly by inhibiting bone resorption, thus having beneficial effects on the skeleton. For example, it has been reported that a number of common vegetables, including onion, garlic and parsley, can inhibit bone resorption in ovariectomized rats. Essential oils derived from sage, rosemary, thyme and other herbs inhibit osteoclast activity in vitro and in vitro and leading to an increase in bone mineral density. Soya, a rich source of isoflavones, has shown promising results and epidemiological evidence to support a use in maintaining bone health, and various traditional herbal formulae in Chinese and Ayurvedic medicine also have demonstrable effects in pharmacological models of osteoporosis. Recently, cannabinoids have been described as having positive effects on osteoblast differentiation, and the presence of cannabinoid receptors in bone tissue indicates a more complex role in bone metabolism than previously thought. The first part of this review briefly discusses normal bone metabolism and disorders caused by its disruption, with particular reference to osteoporosis and current pharmacological treatments. The effects of natural products on bone and connective tissue are then discussed, to include items of diet, herbal extracts and food supplements, with evidence for their efficacy outlined. Copyright (c) 2006 John Wiley & Sons, Ltd.

Cost-effectiveness of a preventive counseling and support package for postnatal depression

Objectives: This study reports the cost-effectiveness of a preventive intervention, consisting of counseling and specific support for the mother-infant relationship, targeted at women at high risk of developing postnatal depression. Methods: A prospective economic evaluation was conducted alongside a pragmatic randomized controlled trial in which women considered at high risk of developing postnatal depression were allocated randomly to the preventive intervention (n = 74) or to routine primary care (n = 77). The primary outcome measure was the duration of postnatal depression experienced during the first 18 months postpartum. Data on health and social care use by women and their infants up to 18 months postpartum were collected, using a combination of prospective diaries and face-to-face interviews, and then were combined with unit costs ( pound, year 2000 prices) to obtain a net cost per mother-infant dyad. The nonparametric bootstrap method was used to present cost-effectiveness acceptability curves and net benefit statistics at alternative willingness to pay thresholds held by decision makers for preventing 1 month of postnatal depression. Results: Women in the preventive intervention group were depressed for an average of 2.21 months (9.57 weeks) during the study period, whereas women in the routine primary care group were depressed for an average of 2.70 months (11.71 weeks). The mean health and social care costs were estimated at 2,396.9 pound per mother-infant dyad in the preventive intervention group and 2,277.5 pound per mother-infant dyad in the routine primary care group, providing a mean cost difference of 119.5 pound (bootstrap 95 percent confidence interval [Cl], -535.4, 784.9). At a willingness to pay threshold of 1,000 pound per month of postnatal depression avoided, the probability that the preventive intervention is cost-effective is .71 and the mean net benefit is 383.4 pound (bootstrap 95 percent Cl, -863.3- pound 1,581.5) pound. Conclusions: The preventive intervention is likely to be cost-effective even at relatively low willingness to pay thresholds for preventing 1 month of postnatal depression during the first 18 months postpartum. Given the negative impact of postnatal depression on later child development, further research is required that investigates the longer-term cost-effectiveness of the preventive intervention in high risk women.

Investigation into low power of a 2D Inverse Discrete CosineTransform (IDCT) in FPGAs

Design for low power in FPGA is rather limited since technology factors affecting power are either fixed or limited for FPGA families. This paper investigates opportunities for power savings of a pipelined 2D IDCT design at the architecture and logic level. We report power consumption savings of over 25% achieved in FPGA circuits obtained from clock gating implementation of optimizations made at the algorithmic level(1).

Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

This work compares and contrasts results of classifying time-domain ECG signals with pathological conditions taken from the MITBIH arrhythmia database. Linear discriminant analysis and a multi-layer perceptron were used as classifiers. The neural network was trained by two different methods, namely back-propagation and a genetic algorithm. Converting the time-domain signal into the wavelet domain reduced the dimensionality of the problem at least 10-fold. This was achieved using wavelets from the db6 family as well as using adaptive wavelets generated using two different strategies. The wavelet transforms used in this study were limited to two decomposition levels. A neural network with evolved weights proved to be the best classifier with a maximum of 99.6% accuracy when optimised wavelet-transform ECG data wits presented to its input and 95.9% accuracy when the signals presented to its input were decomposed using db6 wavelets. The linear discriminant analysis achieved a maximum classification accuracy of 95.7% when presented with optimised and 95.5% with db6 wavelet coefficients. It is shown that the much simpler signal representation of a few wavelet coefficients obtained through an optimised discrete wavelet transform facilitates the classification of non-stationary time-variant signals task considerably. In addition, the results indicate that wavelet optimisation may improve the classification ability of a neural network. (c) 2005 Elsevier B.V. All rights reserved.

A novel radix-3/9 algorithm for type-III generalized discrete Hartley transform

A novel radix-3/9 algorithm for type-III generalized discrete Hartley transform (GDHT) is proposed, which applies to length-3(P) sequences. This algorithm is especially efficient in the case that multiplication is much more time-consuming than addition. A comparison analysis shows that the proposed algorithm outperforms a known algorithm when one multiplication is more time-consuming than five additions. When combined with any known radix-2 type-III GDHT algorithm, the new algorithm also applies to length-2(q)3(P) sequences.

Error estimates for a fully discrete spectral scheme for a class of nonlinear, nonlocal dispersive wave equations

We analyze a fully discrete spectral method for the numerical solution of the initial- and periodic boundary-value problem for two nonlinear, nonlocal, dispersive wave equations, the Benjamin–Ono and the Intermediate Long Wave equations. The equations are discretized in space by the standard Fourier–Galerkin spectral method and in time by the explicit leap-frog scheme. For the resulting fully discrete, conditionally stable scheme we prove an L2-error bound of spectral accuracy in space and of second-order accuracy in time.

On a class of nonselfadjoint periodic boundary value problems with discrete real spectrum

In a previous paper (J. of Differential Equations, Vol. 249 (2010), 3081-3098) we examined a family of periodic Sturm-Liouville problems with boundary and interior singularities which are highly non-self-adjoint but have only real eigenvalues. We now establish Schatten class properties of the associated resolvent operator.

Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

Relationship between deadbeat and pole-placement discrete-time PID controllers

This paper considers PID control in terms of its implementation by means of an ARMA plant model. Two controller actions are considered, namely pole placement and deadbeat, both being applied via a PID structure for the adaptive real-time control of an industrial level system. As well as looking at two controller types separately, a comparison is made between the forms and it is shown how, under certain circumstances, the two forms can be seen to be identical. It is shown how the pole-placement PID form does not in fact realise an action which is equivalent to the deadbeat controller, when all closed-loop poles are chosen to be at the origin of the z-plane.

A critique of neural networks for discrete-time linear control

This paper discusses the use of multi-layer perceptron networks for linear or linearizable, adaptive feedback.control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parametrization. A comparison is made with standard, non-perceptron algorithms, e.g. self-tuning control, and it is shown how gross over-parametrization can occur in the neural network case. Because of the resultant heavy computational burden and poor controller convergence, a strong case is made against the use of neural networks for discrete-time linear control.