Understanding cisplatin resistance using cellular models

Many mechanisms of cisplatin resistance have been proposed from studies of cellular models of resistance including changes in cellular drug accumulation, detoxification of the drug, inhibition of apoptosis and repair of the DNA adducts. A series of resistant models were developed from CCRF-CEM leukaemia cells with increasing doses of cisplatin from 100 ng/ml. This produced increasing resistance up to 7-fold with a treatment dose of 1.6 μg/ml. Cisplatin resistance in these cells correlated with increases in the antioxidant glutathione, yet treatment with buthionine sulphoximine, an inhibitor of glutathione synthesis, had no effect on resistance, suggesting that the increase in glutathione was not directly involved in cisplatin resistance. Two models were developed from H69 SCLC cells, H69-CP and H69CIS200 using 100 ng/ml or 200 ng/ml cisplatin respectively. Both cell models were 2-4 fold resistant to cisplatin, and have decreased expression of p21 which may increase the cell’s ability to progress through the cell cycle in the presence of DNA damage. Both the H69-CP and H69CIS200 cells showed no decrease in cellular cisplatin accumulation. However, the H69-CP cells have increased levels of cellular glutathione and are cross resistant to radiation whereas the H69CIS200 cells have neither of these changes. This suggests that increases in glutathione may contribute to cross-resistance to other drugs and radiation, but not directly to cisplatin resistance. There are multiple resistance mechanisms induced by cisplatin treatment, even in the same cell type. How then should cisplatin-resistant cancers be treated? Cisplatin-resistant cell lines are often more sensitive to another chemotherapeutic drug paclitaxel (H69CIS200), or are able to be sensitised to cisplatin with paclitaxel pre-treatment (H69-CP). The understanding of this sensitisation by paclitaxel using cell models of cisplatin resistance will lead to improvements in the clinical treatment of cisplatin resistant tumours

Comparative performances of stochastic competitive evolutionary neural tree (SCENT) with neural classifiers

A stochastic competitive evolutionary neural tree (SCENT) is described and evaluated against the best neural classifiers with equivalent functionality, using a collection of data sets chosen to provide a variety of clustering scenarios. SCENT is firstly shown to produce flat classifications at least as well as the other two neural classifiers used. Moreover its variability in performance over the data sets is shown to be small. In addition SCENT also produces a tree that can show any hierarchical structure contained in the data. For two real world data sets the tree captures hierarchical features of the data.Peer reviewe

Hierarchical topological clustering learns stock market sectors

The breakdown of financial markets into sectors provides an intuitive classification for groups of companies. The allocation of a company to a sector is an expert task, in which the company is classified by the activity that most closely describes the nature of the company's business. Individual share price movement is dependent upon many factors, but there is an expectation for shares within a market sector to move broadly together. We are interested in discovering if share closing prices do move together, and whether groups of shares that do move together are identifiable in terms of industrial activity. Using TreeGNG, a hierarchical clustering algorithm, on a time series of share closing prices, we have identified groups of companies that cluster into clearly identifiable groups. These clusters compare favourably to a globally accepted sector classification scheme, and in our opinion, our method identifies sector structure clearer than a statistical agglomerative hierarchical clustering metho

High capacity associative memory with bipolar and binary, biased patterns

The high capacity associative memory model is interesting due to its significantly higher capacity when compared with the standard Hopfield model. These networks can use either bipolar or binary patterns, which may also be biased. This paper investigates the performance of a high capacity associative memory model trained with biased patterns, using either bipolar or binary representations. Our results indicate that the binary network performs less well under low bias, but better in other situations, compared with the bipolar network.Peer reviewe

Gender classification of face images: the role of global and feature-based information

Peer reviewe

Connection Strategies in Associative Memory Models

“The original publication is available at www.springerlink.com”. Copyright Springer.The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks.Peer reviewe

Special features of RAD Sequencing data:implications for genotyping

Restriction site-associated DNA Sequencing (RAD-Seq) is an economical and efficient method for SNP discovery and genotyping. As with other sequencing-by-synthesis methods, RAD-Seq produces stochastic count data and requires sensitive analysis to develop or genotype markers accurately. We show that there are several sources of bias specific to RAD-Seq that are not explicitly addressed by current genotyping tools, namely restriction fragment bias, restriction site heterozygosity and PCR GC content bias. We explore the performance of existing analysis tools given these biases and discuss approaches to limiting or handling biases in RAD-Seq data. While these biases need to be taken seriously, we believe RAD loci affected by them can be excluded or processed with relative ease in most cases and that most RAD loci will be accurately genotyped by existing tools

Optimal Design Approach of Solar Powered Rural Water Distribution Systems in Developing Countries

This is the author accepted manuscript.In many rural parts of the developing world reliable access to clean water and electrical power is constrained. In this study, methods of integrating estimations of power outputs from solar photovoltaic arrays into gravity-fed water distribution network modelling are investigated. The effects of powering a rural water distribution system that is replenished with groundwater pumps that use solar power, and the effect of this on other network design decisions, are investigated. A rural community of an estimated 2,800 people with 28 standpipes from a borehole was chosen to develop the optimisations. The water storage tank and pipework were the focus on the water distribution system. EPANET and generic algorithms were used to run network optimisation simulations of: water tank location, elevation and volume; pipe diameter and configuration; and optimal system design in terms of cost. Different scenarios were included producing supply, demand and required water storage curves, which could have practical application for rural water distribution system design. Indicative costs for theoretical water distribution networks for rural communities in The Gambia were generated

Theory and Practice of Translation: An Original Translation of Regina E. G. Schymiczek’s Die Weide Der Seepferde (The Pasture of the Seahorses)

This study outlines the original translation of the recently published Die Weide der Seepferde (2013) from Regina E. G. Schymizcek. We employ known translation techniques by following Hervey’s (2006) suggested gradients of degrees of translation and cultural transposition. While translating the work we discovered a unique aspect of German-English translation which we believe fundamentally adds to knowledge of translation theory