Non-invasive in vivo imaging in small animal research

Non-invasive real time in vivo molecular imaging in small animal models has become the essential bridge between in vitro data and their translation into clinical applications. The tremendous development and technological progress, such as tumour modelling, monitoring of tumour growth and detection of metastasis, has facilitated translational drug development. This has added to our knowledge on carcinogenesis. The modalities that are commonly used include Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), bioluminescence imaging, fluorescence imaging and multi-modality imaging systems. The ability to obtain multiple images longitudinally provides reliable information whilst reducing animal numbers. As yet there is no one modality that is ideal for all experimental studies. This review outlines the instrumentation available together with corresponding applications reported in the literature with particular emphasis on cancer research. Advantages and limitations to current imaging technology are discussed and the issues concerning small animal care during imaging are highlighted.

Robust Speaker Recognition in Noisy Conditions

This paper investigates the problem of speaker identi-fication and verification in noisy conditions, assuming that speechsignals are corrupted by environmental noise, but knowledgeabout the noise characteristics is not available. This research ismotivated in part by the potential application of speaker recog-nition technologies on handheld devices or the Internet. Whilethe technologies promise an additional biometric layer of securityto protect the user, the practical implementation of such systemsfaces many challenges. One of these is environmental noise. Due tothe mobile nature of such systems, the noise sources can be highlytime-varying and potentially unknown. This raises the require-ment for noise robustness in the absence of information about thenoise. This paper describes a method that combines multicondi-tion model training and missing-feature theory to model noisewith unknown temporal-spectral characteristics. Multiconditiontraining is conducted using simulated noisy data with limitednoise variation, providing a “coarse” compensation for the noise,and missing-feature theory is applied to refine the compensationby ignoring noise variation outside the given training conditions,thereby reducing the training and testing mismatch. This paperis focused on several issues relating to the implementation of thenew model for real-world applications. These include the gener-ation of multicondition training data to model noisy speech, thecombination of different training data to optimize the recognitionperformance, and the reduction of the model’s complexity. Thenew algorithm was tested using two databases with simulated andrealistic noisy speech data. The first database is a redevelopmentof the TIMIT database by rerecording the data in the presence ofvarious noise types, used to test the model for speaker identifica-tion with a focus on the varieties of noise. The second database isa handheld-device database collected in realistic noisy conditions,used to further validate the model for real-world speaker verifica-tion. The new model is compared to baseline systems and is foundto achieve lower error rates.

Virtex FPGA Implementation of a Pipelined Adaptive LMS Predictor for Electronic Support Measures Receivers

High-speed field-programmable gate array (FPGA) implementations of an adaptive least mean square (LMS) filter with application in an electronic support measures (ESM) digital receiver, are presented. They employ "fine-grained" pipelining, i.e., pipelining within the processor and result in an increased output latency when used in the LMS recursive system. Therefore, the major challenge is to maintain a low latency output whilst increasing the pipeline stage in the filter for higher speeds. Using the delayed LMS (DLMS) algorithm, fine-grained pipelined FPGA implementations using both the direct form (DF) and the transposed form (TF) are considered and compared. It is shown that the direct form LMS filter utilizes the FPGA resources more efficiently thereby allowing a 120 MHz sampling rate.

Geophysical imaging of stimulated microbial biomineralization

Understanding how microorganisms influence the physical and chemical properties of the subsurface is hindered by our inability to observe microbial dynamics in real time and with high spatial resolution. Here, we investigate the use of noninvasive geophysical methods to monitor biomineralization at the laboratory scale during stimulated sulfate reduction under dynamic flow conditions. Alterations in sediment characteristics resulting from microbe-mediated sulfide mineral precipitation were concomitant with changes in complex resistivity and acoustic wave propagation signatures. The sequestration of zinc and iron in insoluble sulfides led to alterations in the ability of the pore fluid to conduct electrical charge and of the saturated sediments to dissipate acoustic energy. These changes resulted directly from the nucleation, growth, and development of nanoparticulate precipitates along grain surfaces and within the pore space. Scanning and transmission electron microscopy (SEM and TEM) confirmed the sulfides to be associated with cell surfaces, with precipitates ranging from aggregates of individual 3-5 nm nanocrystals to larger assemblages of up to 10-20 m in diameter. Anomalies in the geophysical data reflected the distribution of mineral precipitates and biomass over space and time, with temporal variations in the signals corresponding to changes in the aggregation state of the nanocrystalline sulfides. These results suggest the potential for using geophysical techniques to image certain subsurface biogeochemical processes, such as those accompanying the bioremediation of metal-contaminated aquifers.