Maternal depression and psychiatric outcomes in adolescent offspring: a13-year longitudinal study

Background: Maternal postnatal depression (PND) has been associated with adverse outcomes in young children, but an association with longer-term psychiatric disorder has not been demonstrated. We present the preliminary findings of a 13-year longitudinal study. Methods: In the course of a prospective longitudinal study, we examined DSM-IV Axis I disorders in 13-year-old adolescents who had (n=53) or had not (n=41) been exposed to maternal PND. We also detailed the occurrence of depression in mothers throughout the 13-year follow-up period. Results: Maternal PND was associated with higher rates of affective disorders in adolescent offspring. However, mothers who developed PND were also substantially more likely than those who did not to experience depression subsequently, a fact that contributed to the development of depressive disorder in offspring. Maternal PND was associated with increased risk for depression in adolescent offspring only if there had also been later episodes of maternal depression. In contrast, anxiety disorders in offspring were elevated in the maternal PND group regardless of the occurrence of subsequent maternal depression. Limitations: Due to the modest sample size and consequently limited power, findings must be regarded as preliminary. Conclusions: The particular association between early maternal depression and anxiety disorders in offspring was consistent with theories that emphasise the primacy of early environmental exposures. This position was not supported with respect to offspring depressive disorder, where overall duration of maternal depression was a significant factor. PND was associated with recurrent episodes of depression in the majority of cases, underlining the need for monitoring of this population beyond the postnatal period. (c) 2006 Elsevier B.V. All rights reserved.

Controlled trial of the short- and long-term effect of psychological treatment of post-partum depression - 1. Impact on maternal mood!

Background: Psychological interventions for postnatal depression can be beneficial in the short term but their longer-term impact is unknown, Aims To evaluate the long-term effect on maternal mood of three psychological treatments in relation to routine primary care. Method: Women with post-partum depression (n=193)were assigned randomly to one of four conditions: routine primary care, non-directive counselling, cognitive-behavioural therapy or psychodynamic therapy. They were assessed immediately after the treatment phase (at 4.5 months) and at 18 and 60 months post-partum. Results: Compared with the control, ail three treatments had a significant impact at 4.5 months on maternal mood (Edinburgh Postnatal Depression Scale, EPDS). Only psychodynamic therapy produced a rate of reduction in depression (Structured Clinical interview for DSM III-R) significantly superior to that of the control. The benefit of treatment was no longer apparent by 9 months postpartum, treatment did not reduce subsequent episodes of post-partum depression. Conclusions: Psychological intervention for post-partum depression improves maternal mood (EPDS) in the short term. However, this benefit is not superior to spontaneous remission in the long term.

Inverse model control using recurrent networks

This paper illustrates how internal model control of nonlinear processes can be achieved by recurrent neural networks, e.g. fully connected Hopfield networks. It is shown that using results developed by Kambhampati et al. (1995), that once a recurrent network model of a nonlinear system has been produced, a controller can be produced which consists of the network comprising the inverse of the model and a filter. Thus, the network providing control for the nonlinear system does not require any training after it has been trained to model the nonlinear system. Stability and other issues of importance for nonlinear control systems are also discussed.

Input/output linearization using dynamic recurrent neural networks

This paper brings together two areas of research that have received considerable attention during the last years, namely feedback linearization and neural networks. A proposition that guarantees the Input/Output (I/O) linearization of nonlinear control affine systems with Dynamic Recurrent Neural Networks (DRNNs) is formulated and proved. The proposition and the linearization procedure are illustrated with the simulation of a single link manipulator.

Stable receding horizon control based on recurrent networks

The last decade has seen the re-emergence of artificial neural networks as an alternative to traditional modelling techniques for the control of nonlinear systems. Numerous control schemes have been proposed and have been shown to work in simulations. However, very few analyses have been made of the working of these networks. The authors show that a receding horizon control strategy based on a class of recurrent networks can stabilise nonlinear systems.

Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks

This work provides a framework for the approximation of a dynamic system of the form x˙=f(x)+g(x)u by dynamic recurrent neural network. This extends previous work in which approximate realisation of autonomous dynamic systems was proven. Given certain conditions, the first p output neural units of a dynamic n-dimensional neural model approximate at a desired proximity a p-dimensional dynamic system with n>p. The neural architecture studied is then successfully implemented in a nonlinear multivariable system identification case study.

Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.

A practical approach for training dynamic recurrent neural networks: use of Apriori information

Presents a technique for incorporating a priori knowledge from a state space system into a neural network training algorithm. The training algorithm considered is that of chemotaxis and the networks being trained are recurrent neural networks. Incorporation of the a priori knowledge ensures that the resultant network has behaviour similar to the system which it is modelling.

Internal model control of nonlinear systems through the inversion of recurrent neural networks

Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.

Dynamic input/output linearization using recurrent neural networks

A dynamic recurrent neural network (DRNN) is used to input/output linearize a control affine system in the globally linearizing control (GLC) structure. The network is trained as a part of a closed loop that involves a PI controller, the goal is to use the network, as a dynamic feedback, to cancel the nonlinear terms of the plant. The stability of the configuration is guarantee if the network and the plant are asymptotically stable and the linearizing input is bounded.

Identification of nonlinear systems with a dynamic recurrent neural network

Two approaches are presented to calculate the weights for a Dynamic Recurrent Neural Network (DRNN) in order to identify the input-output dynamics of a class of nonlinear systems. The number of states of the identified network is constrained to be the same as the number of states of the plant.

Relative order defines a topology for recurrent networks

This paper uses techniques from control theory in the analysis of trained recurrent neural networks. Differential geometry is used as a framework, which allows the concept of relative order to be applied to neural networks. Any system possessing finite relative order has a left-inverse. Any recurrent network with finite relative order also has an inverse, which is shown to be a recurrent network.

Children, futurity, and value: 'Adventures of Huckleberry Finn'

A recurrent but relatively unquestioned element in the canonisation of Adventures of Huckleberry Finn is that the novel is about securing a meaningful way forward for the American child. The sense is that the novel deserves to live and to have a future because it is about a child, and tied in with the need for the young nation to project and to determine its future. This might seem, to apply the terms of queer debate, to lend weight to ‘reproductive futurism’: the child and ‘American family values’ are to the fore, while sexual minorities and alternative social models are excluded. The present essay re-reads Huckleberry Finn and Twain’s other Huck narratives, using the coordinates of queer theory. The result is a more equivocal picture. Twain does use Huck to assert the rights of the white American family, but he also uses him to explore alternative ideas of social organisation. More fundamentally, Twain increasingly finds that the idea of the child is no longer a sufficient motive for believing in and projecting a future. Rather, his writing leads the reader towards the impossibility of the future, both for the nation and its child.

Economists are not dismal, the world is not a Petri dish and other reasons for optimism

One of the recurrent themes in the debate around how to ensure global food security concerns the capacity of the planet to support its growing population. Neo-Malthusian thinking suggests that we are in a situation in which further expansion of the population cannot be supported and that the population checks, with their dismal consequences envisaged by Malthus, will lead to a new era of stagnant incomes and population. More sophisticated models of the link between population and income are less gloomy however. They see population growth as an integral component of the economic growth which is necessary to ensure that the poorest achieve food security. An undue focus on the difficulties of meeting the demands of the increasing population risks damaging this growth. Instead, attention should be focused on ensuring that the conditions to ensure that economic growth accompanies population growth are in place.