Birth weight and age at menopause in Australian female twin pairs: exploration of the fetal origin hypothesis

In a twin sample where duration of gestation can be controlled, a specific example of the fetal origins hypothesis concerning association between low birth weight and early age at menopause is explored. The hypothesis is based on the physiologically plausible path from intrauterine growth retardation and reduced numbers of primary follicles to an earlier menopause. The sample comprised 323 Australian female twin pairs where both co-twins had reached menopause naturally and reported on their weight at birth. Regression analysis showed no linear association between the two variables (P = 0.371, r(2) = 0.0009). Intra-pair differences in age at menopause were investigated in the context of relative birth weight of co-twins. In 265 pairs an intra-pair birth a eight difference was reported. In monozygotic (MZ) pairs (n = 168) this allowed for control of genetic effects as well as gestation duration. No significant differences dependent on birth weight relative to co-twin were found for age at natural menopause in either MZ or dizygotic (DZ) twin pairs, even in pairs whose birth weights differed markedly. There was some indication that twins with premature ovarian failure were heavier at birth than twins with normal or later menopausal age. We conclude that the hypothesis that lower birth weight is associated with earlier menopause is not supported by our data.

A machine learning approach to intraday trading on foreign exchange markets

Foreign Exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. In this paper we try to create such a system using Machine learning approach to emulate trader behaviour on the Foreign Exchange market and to find the most profitable trading strategy.

An implementation of genetic algorithms as a basis for a trading system on the foreign exchange market

Foreign exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process is very helpful. In this paper, we try to create such a system with a genetic algorithm engine to emulate trader behaviour on the foreign exchange market and to find the most profitable trading strategy.