The DUB/USP17 deubiquitinating enzymes: A gene family within a tandemly repeated sequence, is also embedded within the copy number variable Beta-defensin cluste

Background: The DUB/USP17 subfamily of deubiquitinating enzymes were originally identified as immediate early genes induced in response to cytokine stimulation in mice (DUB-1, DUB-1A, DUB-2, DUB-2A). Subsequently we have identified a number of human family members and shown that one of these (DUB-3) is also cytokine inducible. We originally showed that constitutive expression of DUB-3 can block cell proliferation and more recently we have demonstrated that this is due to its regulation of the ubiquitination and activity of the 'CAAX' box protease RCE1.

Results: Here we demonstrate that the human DUB/USP17 family members are found on both chromosome 4p16.1, within a block of tandem repeats, and on chromosome 8p23.1, embedded within the copy number variable betadefensin cluster. In addition, we show that the multiple genes observed in humans and other distantly related mammals have arisen due to the independent expansion of an ancestral sequence within each species. However, it is also apparent when sequences from humans and the more closely related chimpanzee are compared, that duplication events have taken place prior to these species separating.

Conclusions: The observation that the DUB/USP17 genes, which can influence cell growth and survival, have evolved from an unstable ancestral sequence which has undergone multiple and varied duplications in the species examined marks this as a unique family. In addition, their presence within the beta-defensin repeat raises the question whether they may contribute to the influence of this repeat on immune related conditions.

QR decomposition-based matrix inversion for high performance embedded MIMO receivers

Real-time matrix inversion is a key enabling technology in multiple-input multiple-output (MIMO) communications systems, such as 802.11n. To date, however, no matrix inversion implementation has been devised which supports real-time operation for these standards. In this paper, we overcome this barrier by presenting a novel matrix inversion algorithm which is ideally suited to high performance floating-point implementation. We show how the resulting architecture offers fundamentally higher performance than currently published matrix inversion approaches and we use it to create the first reported architecture capable of supporting real-time 802.11n operation. Specifically, we present a matrix inversion approach based on modified squared Givens rotations (MSGR). This is a new QR decomposition algorithm which overcomes critical limitations in other QR algorithms that prohibits their application to MIMO systems. In addition, we present a novel modification that further reduces the complexity of MSGR by almost 20%. This enables real-time implementation with negligible reduction in the accuracy of the inversion operation, or the BER of a MIMO receiver based on this.

Ultra-fast processing of gigapixel Tissue MicroArray images using high performance computing.

BACKGROUND:
tissue MicroArrays (TMAs) are a valuable platform for tissue based translational research and the discovery of tissue biomarkers. The digitised TMA slides or TMA Virtual Slides, are ultra-large digital images, and can contain several hundred samples. The processing of such slides is time-consuming, bottlenecking a potentially high throughput platform.
METHODS:
a High Performance Computing (HPC) platform for the rapid analysis of TMA virtual slides is presented in this study. Using an HP high performance cluster and a centralised dynamic load balancing approach, the simultaneous analysis of multiple tissue-cores were established. This was evaluated on Non-Small Cell Lung Cancer TMAs for complex analysis of tissue pattern and immunohistochemical positivity.
RESULTS:
the automated processing of a single TMA virtual slide containing 230 patient samples can be significantly speeded up by a factor of circa 22, bringing the analysis time to one minute. Over 90 TMAs could also be analysed simultaneously, speeding up multiplex biomarker experiments enormously.
CONCLUSIONS:
the methodologies developed in this paper provide for the first time a genuine high throughput analysis platform for TMA biomarker discovery that will significantly enhance the reliability and speed for biomarker research. This will have widespread implications in translational tissue based research.

FPGA Implementation of a Pipelined Gaussian Calculation for HMM-Based Large Vocabulary Speech Recognition

A scalable large vocabulary, speaker independent speech recognition system is being developed using Hidden Markov Models (HMMs) for acoustic modeling and a Weighted Finite State Transducer (WFST) to compile sentence, word, and phoneme models. The system comprises a software backend search and an FPGA-based Gaussian calculation which are covered here. In this paper, we present an efficient pipelined design implemented both as an embedded peripheral and as a scalable, parallel hardware accelerator. Both architectures have been implemented on an Alpha Data XRC-5T1, reconfigurable computer housing a Virtex 5 SX95T FPGA. The core has been tested and is capable of calculating a full set of Gaussian results from 3825 acoustic models in 9.03 ms which coupled with a backend search of 5000 words has provided an accuracy of over 80%. Parallel implementations have been designed with up to 32 cores and have been successfully implemented with a clock frequency of 133?MHz.