Post-partum depression and infant growth in a South African peri-urban settlement

Aim: To examine the association between maternal post-natal depression and infant growth. Background: Infant growth has recently been shown, in two studies from South Asia, to be adversely affected by maternal depression in the early post-partum period. It is uncertain whether a similar association obtains in developing countries outside Asia. Method: A sample of 147 mother–infant dyads was recruited from a peri-urban settlement outside Cape Town and seen at 2 and 18 months post partum. Results: No clear effect of post-partum depression on infant growth was found. Although maternal depression at 2 months was found to be associated with lower infant weight at 18 months, when birthweight was considered this effect disappeared. Conclusions: Possible explanations for the non-replication of the South Asian findings are considered.

The mother-infant relationship and infant attachment in a south African peri-urban settlement

A sample of 147 mother-infant dyads was recruited from a peri-urban settlement outside Cape Town and seen at 2- and 18-months postpartum. At 18 months, 61.9% of the infants were rated as securely attached (B); 4.1% as avoidant (A); 8.2% as resistant (C); and 25.8% disorganized (D). Postpartum depression at 2 months, and indices of poor parenting at both 2 and 18 months, were associated with insecure infant attachment. The critical 2-month predictor variables for insecure infant attachment were maternal intrusiveness and maternal remoteness, and early maternal depression. When concurrent maternal sensitivity was considered, the quality of the early mother-infant relationship remained important, but maternal depression was no longer predictive. Cross-cultural differences and consistencies in the development of attachment are discussed.

The factor structure of the Edinburgh postnatal depression scale in a South African peri-urban settlement

The factor structure of the Edinburgh Postnatal Depression scale (EPDS) and similar instruments have received little attention in the literature. The researchers set out to investigate the construct validity and reliability of the EPDS amongst impoverished South African women. The EPDS was translated into isiXhosa (using Brislin's back translation method) and administered by trained interviewers to 147 women in Khayelitsha, South Africa. Responses were subjected to maximum likelihood confirmatory factor analysis. A single factor structure was found, consistent with the theory on which the EPDS was based. Internal consistency was satisfactory (a = 0.89).

Fully complex-valued radial basis function networks for orthogonal least squares regression

We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Fully complex-valued radial basis function networks: orthogonal least squares regression and classification

We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully CVRBF network. The proposed fully CVRBF network is also applied to four-class classification problems that are typically encountered in communication systems. A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well. The effectiveness of the proposed algorithm is demonstrated using the example of nonlinear beamforming for multiple-antenna aided communication systems that employ complex-valued quadrature phase shift keying modulation scheme. (C) 2007 Elsevier B.V. All rights reserved.

Orthogonal-least-squares regression: A unified approach for data modelling

A unified approach is proposed for data modelling that includes supervised regression and classification applications as well as unsupervised probability density function estimation. The orthogonal-least-squares regression based on the leave-one-out test criteria is formulated within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic data-modelling approach for constructing parsimonious kernel models with excellent generalisation capability. (C) 2008 Elsevier B.V. All rights reserved.

Retrieval of global atmospheric electrical activity at a polluted urban site

The atmospheric electrical Potential Gradient (PG) arises from global thunderstorm activity, but surface measurements of the atmospheric Potential Gradient (PG) are influenced by global thunderstorms and local aerosol concentration changes. The local aerosol change can be monitored independently, and in some cases the concentration changes are closely related to PG changes. For these circumstances, a general theory to remove the local aerosol influence on PG measurements has been developed. Continuous measurements of PG and aerosol mass concentration were made during 24–31 Dec, 2005 within an urban environment at Reading, UK. The average diurnal variation of PG showed a double diurnal cycle, with maxima in the early morning and evening hours. The aerosol concentration has similar double maxima. Removing the aerosol using from the PG and aerosol correlation returns a single diurnal cycle, suggestive of the more global PG diurnal cycle.

Nocturnal ranging behaviour of urban hedgehogs, Erinaceus europaeus, in relation to risk and reward

Urban areas have both positive and negative influences on wildlife. For terrestrial mammals, one of the principle problems is the risk associated with moving through the environment whilst foraging. In this study, we examined nocturnal patterns of movement of urban-dwelling hedgehogs (Erinaceus europaeus) in relation to (i) the risks posed by predators and motor vehicles and (ii) nightly weather patterns. Hedgehogs preferentially utilised the gardens of semi-detached and terraced houses. However, females, but not males, avoided the larger back gardens of detached houses, which contain more of the habitat features selected by badgers. This difference in the avoidance of predation risk is probably associated with sex differences in breeding behaviour. Differences in nightly movement patterns were consistent with strategies associated with mating behaviour and the accumulation of fat reserves for hibernation. Hedgehogs also exhibited differences in behaviour associated with the risks posed by humans; they avoided actively foraging near roads and road verges, but did not avoid crossing roads per se. They were, however, significantly more active after midnight when there was a marked reduction in vehicle and foot traffic. In particular, responses to increased temperature, which is associated with increased abundance of invertebrate prey, were only observed after midnight. This variation in the timing of bouts of activity would reduce the risks associated with human activities. There were also profound differences in both area ranged and activity with chronological year which warrant further investigation.