Towards a Virtual-Acoustic String Instrument

In acoustic instruments, the controller and the sound producing system often are one and the same object. If virtualacoustic instruments are to be designed to not only simulate the vibrational behaviour of a real-world counterpart but also to inherit much of its interface dynamics, it would make sense that the physical form of the controller is similar to that of the emulated instrument. The specific physical model configuration discussed here reconnects a (silent) string controller with a modal synthesis string resonator across the real and virtual domains by direct routing of excitation signals and model parameters. The excitation signals are estimated in their original force-like form via careful calibration of the sensor, making use of adaptive filtering techniques to design an appropriate inverse filter. In addition, the excitation position is estimated from sensors mounted under the legs of the bridges on either end of the prototype string controller. The proposed methodology is explained and exemplified with preliminary results obtained with a number of off-line experiments.

Generation of an epigenetic signature by chronic hypoxia in prostate cells

Increasing levels of tissue hypoxia have been reported as a natural feature of the aging prostate gland and may be a risk factor for the development of prostate cancer. In this study, we have used PwR-1E benign prostate epithelial cells and an equivalently aged hypoxia-adapted PwR-1E sub-line to identify phenotypic and epigenetic consequences of chronic hypoxia in prostate cells. We have identified a significantly altered cellular phenotype in response to chronic hypoxia as characterized by increased receptor-mediated apoptotic resistance, the induction of cellular senescence, increased invasion and the increased secretion of IL-1 beta, IL6, IL8 and TNFalpha cytokines. In association with these phenotypic changes and the absence of HIF-1 alpha protein expression, we have demonstrated significant increases in global levels of DNA methylation and H3K9 histone acetylation in these cells, concomitant with the increased expression of DNA methyltransferase DMNT3b and gene-specific changes in DNA methylation at key imprinting loci. In conclusion, we have demonstrated a genome-wide adjustment of DNA methylation and histone acetylation under chronic hypoxic conditions in the prostate. These epigenetic signatures may represent an additional mechanism to promote and maintain a hypoxic-adapted cellular phenotype with a potential role in tumour development.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.