Abstract: Near Infrared Spectroscopy (NIRS) allows noninvasive monitoring of tissue oxygenation and cerebral haemodynamics. It is particularly well suited to young developmental populations from birth to toddler years. NIRS can be used to measure in vivo changes oxygenated haemoglobin (Δ[HbO2]), deoxygenated haemoglobin (Δ[HHb]), and oxidised cytochrome-c-oxidase (Δ[oxCCO]), an enzyme in the mitochondria associated with tissue metabolism. The aims of this report are to explore the data, apply computational methods to EEG and NIRS measurements in the time and frequency domains to investigate neurovascular and neurometabolic coupling during seizures, and provide a basis for hypothesis generation. Preliminary results suggest that NIRS data can be used for seizure detection and monitoring conditions before and after seizures, and that NIRS data can confirm the temporal and spatial order in which each seizure spreads.

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Abstract: In contrast to a hippocampal place cell, a grid cell has multiple firing fields with regular spacing that tessellates the environment with a hexagonal pattern. Directional grid cells (\conjunctive grid cells") respond selectively to head direction and location. The results in this report suggest: (1) A spike rate map’s centre of mass vector (CoM) directionality is correlated with preferred head direction (HD) when specific thresholds for gridness, CoM vector magnitude, and HD vector magnitude are applied. However, simply increasing the gridness threshold does not increase correlation. (2) Global grid shifts of all fields contribute more to the magnitude of the centre of mass vector of a spike rate map than local shifts based on a small number of fields with high spike rates.

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Abstract: Classic spike sorting pipelines cluster spike waveforms into groups that represent their source neurons, but they suffer from inaccuracy and incomplete automation. Artificial neural networks (ANNs) are class of machine learning methods that are remarkably flexible and well suited to learning from large data sets. This report presents empirical evidence that neural network enhanced pipelines can provide higher classification accuracy than pipelines that rely exclusively traditional feature extraction and clustering methods.

Abstract: Methods from comparative genomics, sequence analysis, and virtual screening were combined to predict new drug targets and the chemical compounds that bind most strongly to those targets.
Motivation: The evolution and spread of antibiotic resistant pathogenic bacteria has been rapid and often lethal, while the pipeline for new antibiotics has remained virtually bone dry. We face an urgent need for new antibiotics and more cost effective methods to support drug discovery; greater efficiency may be achieved by prioritizing in vitro testing.
Results: This combination of methods provided a cost effective way to predict suitable drug targets, their active sites, and prioritize chemical compounds for in vitro testing of antimicrobial properties, especially in the light of domain expertise.

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Abstract: We present a newly developed multichannel broadband NIRS (or bNIRS) system that has the capacity to measure changes in light attenuation of 308 NIR wavelengths (610nm to 918nm) simultaneously over 16 different brain locations. To achieve this the instrument uses a lens based spectrometer with a front-illuminated CCD that has a sensor size of 26.8x26mm. This large CCD detector allows the simultaneous binning of 16 detector fibres. The software uses the UCLn algorithm to quantify the changes in oxy-, deoxy- haemoglobin concentration (HbO2, HHb) and cytochrome-c-oxidase (oxCCO) simultaneously over 16 different brain locations with 1 second sampling rate. We demonstrate the use of the instrument in quantifying brain tissue oxygenation and metabolic activity simultaneously with electrical changes as measured with EEG in children with seizures.

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Abstract: This paper presents a fully distributed asynchronous clustering protocol for the self-organisation of a wireless sensor network into an infrastructure of well separated cluster heads that supports in-network processing, routing, and deployment. In this protocol, nodes volunteer asynchronously for cluster head duty and use a radio beacon to pre- emptively recruit members. Limited beacon range is used as the primary parameter for self- organisation. The resulting topology substantially reduces total transmission distance and the expected energy consumed by radio communication. To further extend network lifetime and capability, well separated cluster heads may be easily located and replaced by more powerful devices.

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Abstract: This paper presents a paradigm for reducing communication costs in wireless sensor networks. The first component is our Distributed Asynchronous Clustering protocol (DAC), which self-organises the network into an infrastructure that supports in-network processing, routing, and deployment. The second component, and the focus of this paper, is a data-centric routing protocol where cluster heads build and maintain sets of Bloom filters to inform routing decisions and filter out unproductive messages. While other data-centric protocols use a flat topology and rely to some extent on flooding, our protocol exploits a two tier hierarchy to provide an adaptable, scalable, and intelligent routing service that is expected to reduce the number of transmissions and extend network lifetime.

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