Intraovarian actions of oestrogen

Oestrogen regulates several hypothalamic and pituitary hormones, which in turn control ovarian functions. Oestrogen and its metabolites, such as catecholoestrogens, also have direct effects within the ovary. This review examines the roles of oestrogen in regulating ovarian folliculogenesis, ovulation and corpus luteum formation. Oestrogen promotes follicular development, which culminates in ovulation, by potentiating follicular development, granulosa cell expression of gonadotrophin receptors, steroidogenesis, and gap junction formation by granulosa cells, and by inhibiting granulosa cell apoptosis. In addition, oestrogen may be needed for corpus luteum formation and maintenance. Studies on mutant mice that either lack one or both of the known oestrogen receptors or are unable to synthesize oestrogen support some but not all of these prior inferences of the roles of oestrogen within the ovary. Although these transgenic mice have proved useful in determining some of the intraovarian actions of oestrogen, they present confounding problems, including hormonal imbalances, that hinder interpretation. Transgenic mice with conditional or tissue-directed mutations in their oestrogen receptors are needed to dissect the ovarian actions of oestrogen further. In addition, microarray technologies, combined with specific hormone treatment regimens are likely to provide an attractive, alternative approach to using mutant mice in clarifying the direct actions of oestrogen in the ovaries of other species.     

Numerical modelling of a bromide-polysulphide redox flow battery. Part 2: Evaluation of a utility-scale system

Numerical modelling of redox flow battery (RFB) systems allows the technical and commercial performance of different designs to be predicted without costly lab, pilot and full-scale testing. A numerical model of a redox flow battery was used in conjunction with a simple cost model incorporating capital and operating costs to predict the technical and commercial performance of a 120 MWh/15 MW utility-scale polysulphide-bromine (PSB) storage plant for arbitrage applications. Based on 2006 prices, the system was predicted to make a net loss of 0.45 p kWh⁻¹ at an optimum current density of 500 A m⁻² and an energy efficiency of 64%. The system was predicted to become economic for arbitrage (assuming no further costs were incurred) if the rate constants of both electrolytes could be increased to 10⁻⁵ m s⁻¹, for example by using a suitable (low cost) electrocatalyst. The economic viability was found to be strongly sensitive to the costs of the electrochemical cells and the electrical energy price differential.     

Improving product launch times and product design through a service assembly environment

Surely it must be possible to bring products to market faster and do it cheaper? These two requirements have for many years challenged the entire telecommunications industry. Complexity is a characteristic of today’s communications environments. Making this complexity manageable is the key to enabling these requirements to be met, but how? Within the intelligent network’s area it has been possible to break down the needs of the customer into a series of blocks that can be designed and deployed in flexible arrangements. To design and run these blocks two environments are used, the service assembly environment (SAE) and the service execution environment. This paper will look at the former to examine what steps need to be taken to apply these principles to the full range of communications products that a large telecommunications company like BT offer. This paper defines the SAE as including both the design phase of a product as well as its deployment into the portfolio and systems.     

Independent component analysis: A flexible nonlinearity and decorrelating manifold approach

Independent component analysis (ICA) finds a linear transformation to variables that are maximally statistically independent. We examine ICA and algorithms for finding the best transformation from the point of view of maximizing the likelihood of the data. In particular, we discuss the way in which scaling of the unmixing matrix permits a "static" nonlinearity to adapt to various marginal densities. We demonstrate a new algorithm that uses generalized exponential functions to model the marginal densities and is able to separate densities with light tails. We characterize the manifold of decorrelating matrices and show that it lies along the ridges of high-likelihood unmixing matrices in the space of all unmixing matrices. We show how to find the optimum ICA matrix on the manifold of decorrelating matrices, and as an example we use the algorithm to find independent component basis vectors for an ensemble of portraits.