Docosahexaenoic Acid Improves the Nitroso-Redox Balance and Reduces VEGF-Mediated Angiogenic Signaling in Microvascular Endothelial Cells

Purpose. Disturbances to the cellular production of nitric oxide (NO) and superoxide (O2-) can have deleterious effects on retinal vascular integrity and angiogenic signaling. Dietary agents that could modulate the production of these signaling molecules from their likely enzymatic sources, endothelial nitric oxide synthase (eNOS) and NADPH oxidase, would therefore have a major beneficial effect on retinal vascular disease. The effect of ?-3 polyunsaturated fatty acids (PUFAs) on angiogenic signaling and NO/superoxide production in retinal microvascular endothelial cells (RMECs) was investigated.

Methods. Primary RMECs were treated with docosahexaenoic acid (DHA) or eicosapentaenoic acid (EPA) for 48 hours. RMEC migration was determined by scratch-wound assay, proliferation by the incorporation of BrdU, and angiogenic sprouting using a three-dimensional model of in vitro angiogenesis. NO production was quantified by Griess assay, and phospho-eNOS accumulation and superoxide were measured using the fluorescent probe dihydroethidine. eNOS localization to caveolin-rich microdomains was determined by Western blot analysis after subfractionation on a linear sucrose gradient.

Results. DHA treatment increased nitrite and decreased superoxide production, which correlated with the displacement of eNOS from caveolar subdomains and colocalization with the negative regulator caveolin-1. In addition, both ?-3 PUFAs demonstrated reduced responsiveness to VEGF-stimulated superoxide and nitrite release and significantly impaired endothelial wound healing, proliferation, and angiogenic sprout formation.

Conclusions. DHA improves NO bioavailability, decreases O2- production, and blunts VEGF-mediated angiogenic signaling. These findings suggest a role for ?-3 PUFAs, particularly DHA, in maintaining vascular integrity while reducing pathologic retinal neovascularization.

Power Efficient Dynamic-Range Utilisation for DSP On FPGA

A power and resource efficient ‘dynamic-range utilisation’ technique to increase operational capacity of DSP IP cores by exploiting redundancy in the data epresentation of sampled analogue input data, is presented. By cleverly partitioning dynamic-range into separable processing threads, several data streams are computed concurrently on the same hardware. Unlike existing techniques which act solely to reduce power consumption due to sign extension, here the dynamic range is exploited to increase operational capacity while still achieving reduced power consumption. This extends an existing system-level, power efficient framework for the design of low power DSP IP cores, which when applied to the design of an FFT IP core in a digital receiver system gives an architecture requiring 50% fewer multipliers, 12% fewer slices and 51%-56% less power.

Power Efficient DSP Datapath Configuration Methodology for FPGA

Exploiting the underutilisation of variable-length DSP algorithms during normal operation is vital, when seeking to maximise the achievable functionality of an application within peak power budget. A system level, low power design methodology for FPGA-based, variable length DSP IP cores is presented. Algorithmic commonality is identified and resources mapped with a configurable datapath, to increase achievable functionality. It is applied to a digital receiver application where a 100% increase in operational capacity is achieved in certain modes without significant power or area budget increases. Measured results show resulting architectures requires 19% less peak power, 33% fewer multipliers and 12% fewer slices than existing architectures.

Memory-Centric Hardware Synthesis from Dataflow Models

Generation of hardware architectures directly from dataflow representations is increasingly being considered as research moves toward system level design methodologies. Creation of networks of IP cores to implement actor functionality is a common approach to the problem, but often the memory sub-systems produced using these techniques are inefficiently utilised. This paper explores some of the issues in terms of memory organisation and accesses when developing systems from these high level representations. Using a template matching design study, challenges such as modelling memory reuse and minimising buffer requirements are examined, yielding results with significantly less memory requirements and costly off-chip memory accesses.

Feature Selection for Pose Invariant Lip Biometrics

For the first time in this paper the authors present results showing the effect of out of plane speaker head pose variation on a lip biometric based speaker verification system. Using appearance DCT based features, they adopt a Mutual Information analysis technique to highlight the class discriminant DCT components most robust to changes in out of plane pose. Experiments are conducted using the initial phase of a new multi view Audio-Visual database designed for research and development of pose-invariant speech and speaker recognition. They show that verification performance can be improved by substituting higher order horizontal DCT components for vertical, particularly in the case of a train/test pose angle mismatch.

Inter-Frame Contextual Modelling For Visual Speech Recognition

In this paper, we present a new approach to visual speech recognition which improves contextual modelling by combining Inter-Frame Dependent and Hidden Markov Models. This approach captures contextual information in visual speech that may be lost using a Hidden Markov Model alone. We apply contextual modelling to a large speaker independent isolated digit recognition task, and compare our approach to two commonly adopted feature based techniques for incorporating speech dynamics. Results are presented from baseline feature based systems and the combined modelling technique. We illustrate that both of these techniques achieve similar levels of performance when used independently. However significant improvements in performance can be achieved through a combination of the two. In particular we report an improvement in excess of 17% relative Word Error Rate in comparison to our best baseline system.