Biomedical subjects

Joos Vandewalle

Publications and source records attributed to Joos Vandewalle.

3 recordsLinked to original sources

PubMed2004

A possible role of the cytochrome P450c17alpha gene (CYP17) polymorphism in the pathobiology of uterine leiomyomas from black South African women: a pilot study.

BACKGROUND: To investigate the role of the CYP17 gene promoter polymorphism in the pathobiology of uterine leiomyomas in African and Caucasian women. METHODS: During a 6-month period, 145 Caucasian and black South African women undergoing hysterectomy were included prospectively. Blood samples were obtained for DNA analysis. Factors modifying the risk for uterine leiomyoma growth such as age, parity, age at last birth, weight, body mass index, menopausal status, cigarette smoking and oral contraceptive use were determined. RESULTS: The risk for leiomyoma development among black South African homozygous carriers of the CYP17 A2 allele was shown to be significantly increased when compared to women homozygous for the CYP17 A1 allele or to heterozygous women. Logistic regression analysis disclosed age, parity and CYP17 polymorphism to have an impact on the presence of uterine leiomyomas (p-values are, respectively, 0.0006, 0.0001 and 0.03) in black South African women. However, among Caucasian women, logistic regression analysis showed only intake of oral contraceptives to influence the formation of uterine leiomyomas (p = 0.03). CONCLUSION: This exploratory trial suggests that among African women, homozygous carriers of the CYP17 A2 allele expose their myometrium to a stronger estrogenic stimulation contributing to the pathobiology of uterine leiomyomas.

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PubMed1999

Embedding recurrent neural networks into predator-prey models.

We study changes of coordinates that allow the embedding of ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models-also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form (Brenig, L. (1988). Complete factorization and analytic solutions of generalized Lotka-Volterra equations. Physics Letters A, 133(7-8), 378-382), where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoid. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network. We expect that this transformation will permit the application of existing techniques for the analysis of Lotka-Volterra systems to recurrent neural networks. Furthermore, our results show that Lotka-Volterra systems are universal approximators of dynamical systems, just as are continuous-time neural networks.

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PubMed1997

NL(q) Theory: A Neural Control Framework with Global Asymptotic Stability Criteria.

In this paper a framework for model-based neural control design is presented, consisting of nonlinear state space models and controllers, parametrized by multilayer feedforward neural networks. The models and closed-loop systems are transformed into so-called NL(q) system form. NL(q) systems represent a large class of nonlinear dynamical systems consisting of q layers with alternating linear and static nonlinear operators that satisfy a sector condition. For such NL(q)s sufficient conditions for global asymptotic stability, input/output stability (dissipativity with finite L(2)-gain) and robust stability and performance are presented. The stability criteria are expressed as linear matrix inequalities. In the analysis problem it is shown how stability of a given controller can be checked. In the synthesis problem two methods for neural control design are discussed. In the first method Narendra's dynamic backpropagation for tracking on a set of specific reference inputs is modified with an NL(q) stability constraint in order to ensure, e.g., closed-loop stability. In a second method control design is done without tracking on specific reference inputs, but based on the input/output stability criteria itself, within a standard plant framework as this is done, for example, in H( infinity ) control theory and &mgr; theory. Copyright 1997 Elsevier Science Ltd.

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