Introduction to Discounted Cash Flow Analysis and Financial Functions in Excel

The financial and economic analysis of investment projects is typically carried out using the technique of discounted cash flow (DCF) analysis. This module introduces concepts of discounting and DCF analysis for the derivation of project performance criteria such as net present value (NPV), internal rate of return (IRR) and benefit to cost (B/C) ratios. These concepts and criteria are introduced with respect to a simple example, for which calculations using MicroSoft Excel are demonstrated.

Analysis of the TGF beta functional pathway in epithelial ovarian carcinoma

Epithelial ovarian carcinoma is often diagnosed at an advanced stage of disease and is the leading cause of death from gynaecological neoplasia. The genetic changes that occur during the development of this carcinoma are poorly understood. It has been proposed that IGFIIR, TGF beta1 and TGF beta RII act as a functional unit in the TGF beta growth inhibitory pathway, and that somatic loss-of-function mutations in any one of these genes could lead to disruption of the pathway and subsequent loss of cell cycle control. We have examined these 3 genes in 25 epithelial ovarian carcinomas using single-stranded conformational polymorphism analysis and DNA sequence analysis. A total of 3 somatic missense mutations were found in the TGF beta RII gene, but none in IGFRII or TGF beta1. An association was found between TGF beta RII mutations and histology, with 2 out of 3 clear cell carcinomas having TGF beta RII mutations. This data supports other evidence from mutational analysis of the PTEN and beta -catenin genes that there are distinct developmental pathways responsible for the progression of different epithelial ovarian cancer histologic subtypes. (C) 2001 Cancer Research Campaign.

Generation of eigenstates using the phase-estimation algorithm

The phase estimation algorithm is so named because it allows an estimation of the eigenvalues associated with an operator. However, it has been proposed that the algorithm can also be used to generate eigenstates. Here we extend this proposal for small quantum systems, identifying the conditions under which the phase-estimation algorithm can successfully generate eigenstates. We then propose an implementation scheme based on an ion trap quantum computer. This scheme allows us to illustrate two simple examples, one in which the algorithm effectively generates eigenstates, and one in which it does not.