Development, Characterization, and Application of a Cadmium-Selective Microelectrode for the Measurement of Cadmium Fluxes in Roots of Thlaspi Species and Wheat1

A Cd2+-selective vibrating microelectrode was constructed using a neutral carrier-based Cd ionophore to investigate ion-transport processes along the roots of wheat (Triticum aestivum L.) and two species of Thlaspi, one a Zn/Cd hyperaccumulator and the other a related nonaccumulator. In simple Cd(NO3)2 solutions, the electrode exhibited a Nernstian response in solutions with Cd2+ activities as low as 50 nm. Addition of Ca2+ to the calibration solutions did not influence the slope of the calibration curve but reduced the detection limit to a solution activity of 1 μm Cd2+. Addition of high concentrations of K+ and Mg2+ to the calibration solution to mimic the ionic composition of the cytoplasm affected neither the slope nor the sensitivity of the electrode, demonstrating the pH-insensitive electrode's potential for intracellular investigations. The electrode was assayed for selectivity and was shown to be at least 1000 times more selective for Cd2+ than for any of those potentially interfering ions tested. Flux measurements along the roots of the two Thlaspi species showed no differences in the pattern or the magnitude of Cd2+ uptake within the time frame considered. The Cd2+-selective microelectrode will permit detailed investigations of heavy-metal ion transport in plant roots, especially in the area of phytoremediation.

Detection of exocytosis at individual pancreatic beta cells by amperometry at a chemically modified microelectrode.

Amperometry at a carbon fiber microelectrode modified with a composite of ruthenium oxide and cyanoruthenate was used to monitor chemical secretions of single pancreatic beta cells from rats and humans. When the insulin secretagogues glucose, tolbutamide, and K+ were applied to the cell, a series of randomly occurring current spikes was observed. The current spikes were shown to be due to the detection of chemical substances secreted from the cell. Chromatography showed that the primary secreted substance detected by the electrode was insulin. The current spikes were strongly dependent on external Ca2+, had an average area that was independent of the stimulation method, and had an area distribution which corresponded to the distribution of vesicle sizes in beta cells. It was concluded that the spikes were due to the detection of concentration pulses of insulin secreted by exocytosis.

A microelectrode impedance method to measure interaction of cells

An impedance method was developed to determine how immune system cells (hemocyte) interact with intruder cells (parasites). When the hemocyte cells interact with the parasites, they cause a defensive reaction and the parasites start to aggregate in clusters. The level of aggregation is a measure of the host-parasite interaction, and provides information about the efficiency of the immune system response. The cell aggregation is monitored using a set of microelectrodes. The impedance spectrum is measured between each individual microelectrode and a large reference electrode. As the cells starts to aggregate and settle down towards the microelectrode array the impedance of the system is changed. It is shown that the system impedance is very sensitive to the level of cell aggregation and can be used to monitor in real time the interaction between hemocyte cells and parasites.

Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease

Mixture models are a flexible tool for unsupervised clustering that have found popularity in a vast array of research areas. In studies of medicine, the use of mixtures holds the potential to greatly enhance our understanding of patient responses through the identification of clinically meaningful clusters that, given the complexity of many data sources, may otherwise by intangible. Furthermore, when developed in the Bayesian framework, mixture models provide a natural means for capturing and propagating uncertainty in different aspects of a clustering solution, arguably resulting in richer analyses of the population under study. This thesis aims to investigate the use of Bayesian mixture models in analysing varied and detailed sources of patient information collected in the study of complex disease. The first aim of this thesis is to showcase the flexibility of mixture models in modelling markedly different types of data. In particular, we examine three common variants on the mixture model, namely, finite mixtures, Dirichlet Process mixtures and hidden Markov models. Beyond the development and application of these models to different sources of data, this thesis also focuses on modelling different aspects relating to uncertainty in clustering. Examples of clustering uncertainty considered are uncertainty in a patient’s true cluster membership and accounting for uncertainty in the true number of clusters present. Finally, this thesis aims to address and propose solutions to the task of comparing clustering solutions, whether this be comparing patients or observations assigned to different subgroups or comparing clustering solutions over multiple datasets. To address these aims, we consider a case study in Parkinson’s disease (PD), a complex and commonly diagnosed neurodegenerative disorder. In particular, two commonly collected sources of patient information are considered. The first source of data are on symptoms associated with PD, recorded using the Unified Parkinson’s Disease Rating Scale (UPDRS) and constitutes the first half of this thesis. The second half of this thesis is dedicated to the analysis of microelectrode recordings collected during Deep Brain Stimulation (DBS), a popular palliative treatment for advanced PD. Analysis of this second source of data centers on the problems of unsupervised detection and sorting of action potentials or "spikes" in recordings of multiple cell activity, providing valuable information on real time neural activity in the brain.

Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

A low-noise low-power noise-adaptive neural amplifier in 0.13um CMOS technology

Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces and in elucidating human neurophysiology. The advent of multichannel microelectrode arrays has driven the need for electronics to record neural signals from many neurons. The dynamic range of the system is limited by background system noise which varies over time. We propose a neural amplifier in UMC 130 nm, 2P8M CMOS technology. It can be biased adaptively from 200 nA to 2 uA, modulating input referred noise from 9.92 uV to 3.9 uV. We also describe a low noise design technique which minimizes the noise contribution of the load circuitry. The amplifier can pass signal from 5 Hz to 7 kHz while rejecting input DC offsets at electrode-electrolyte interface. The bandwidth of the amplifier can be tuned by the pseudo-resistor for selectively recording low field potentials (LFP) or extra cellular action potentials (EAP). The amplifier achieves a mid-band voltage gain of 37 dB and minimizes the attenuation of the signal from neuron to the gate of the input transistor. It is used in fully differential configuration to reject noise of bias circuitry and to achieve high PSRR.

Fabrication of microelectrodes deeply embedded in LiNbO3 using a femtosecond laser

We present a novel technique to fabricate deeply embedded microelectrodes in LiNbO3 using femtosecond pulsed laser ablation and selective electroless plating. The fabrication process mainly consists of four steps, which are (1) micromachining of microgrooves on the surface of LiNbO3 by femtosecond laser ablation; (2) formation of AgNO3 films on substrates; (3) scanning the femtosecond laser beam in the fabricated microgrooves for modi. cation of the inner surfaces; and (4) electroless copper plating. The void-free electroless copper plating is obtained with appropriate cross section of microgrooves and uniform initiation of the autocatalytic deposition on the inner surface of grooves. The dimension and shape of the microelectrodes could be accurately controlled by changing the conditions of femtosecond laser ablation, which in turn can control the distribution of electric field inside LiNbO3 crystal for various applications, opening up a new approach to fabricate three-dimensional integrated electro-optic devices. (C) 2008 Elsevier B. V. All rights reserved.