Clustered components analysis for functional MRI

A common method of increasing hemodynamic response (SNR) in functional magnetic resonance imaging (fMRI) is to average signal timecourses across voxels. This technique is potentially problematic because the hemodynamic response may vary across the brain. Such averaging may destroy significant features in the temporal evolution of the fMRI response that stem from either differences in vascular coupling to neural tissue or actual differences in the neural response between two averaged voxels. Two novel techniques are presented in this paper in order to aid in an improved SNR estimate of the hemodynamic response while preserving statistically significant voxel-wise differences. The first technique is signal subspace estimation for periodic stimulus paradigms that involves a simple thresholding method. This increases SNR via dimensionality reduction. The second technique that we call clustered components analysis is a novel amplitude-independent clustering method based upon an explicit statistical data model. It includes an unsupervised method for estimating the number of clusters. Our methods are applied to simulated data for verification and comparison to other techniques. A human experiment was also designed to stimulate different functional cortices. Our methods separated hemodynamic response signals into clusters that tended to be classified according to tissue characteristics.     

Can we learn anything from single-channel unaveraged MEG data?

A method for the decomposition of single-channel unaveraged magnetoencephalographic (MEG) data into statistically independent components is presented. The study of MEG recordings is characterised by a host of difficulties, most of which stem from the inherently noisy recording process by which the data is obtained. MEG time series typically contain a mix of artifactual components from a variety of sources, and the isolation of interesting signals from this noise background poses a difficult problem. In this article, we present a novel approach combining the techniques of independent component analysis (ICA) and dynamical embedding, which can be used to extract and isolate components of interest from single-channel unaveraged MEG data. In our approach, the method of delays is proposed as a means of augmenting the single-channel data, thus, facilitating the application of ICA. Finally, because the single-channel approach yields no information regarding the physiological origins of extracted sources, we discuss a method by which extracted sources may be projected back into the multichannel measurement space, permitting an estimate of the respective spatial distributions to be obtained. The proposed methods are tested on three separate MEG channels and the results are presented and discussed.     

Metabolite-sensitive holographic biosensors

A new type of biosensor that combines the inexpensiveness and mass-produceability of reflection holograms with the selectivity and specificity of enzymes is described. pH-sensitive holographic sensors were fabricated from ionizable monomers incorporated into thin, polymeric, hydrogel films which were transformed into volume holograms using a diffusion method coupled with holographic recording, using a frequency-doubled Nd:YAG laser (532 nm). These holograms were used as transducer systems to monitor the pH changes associated with specific enzymatic reactions to construct prototype urea- and penicillin-sensitive biosensors. The diffraction wavelength (color) of the holographic biosensors was used to characterize their shrinkage and swelling behavior as a function of analyte concentration. The potential of these sensors for the measurement of the clinically and industrially important metabolites urea and penicillin G is demonstrated.     

The role of elevated temperatures in the implantation of GaAs

The improvement in conductivity resulting from implanting n-type dopants into GaAs at elevated temperatures has been investigated. Measurement of the rate of introducing compensating defects at elevated temperatures shows only marginal reduction from the room temperature rate. The avoidance of lattice disorder buildup also has minimal effect on carrier recovery and the amount of residual compensation after annealing. It appears that compensation cannot account for the improved conductivity of heated implants and that alternatively a substantial fraction of the room temperature introduced ions, even with annealing, must fail to become substitutional in the first instance. For sulfur, the same temperature dependence consistent with the established behavior of selenium and tellurium is obtained. An increase in conductivity by a factor of 2·5 occurs ? 150°C to give up to 40 per cent utilization from 10¹⁴ S⁺ cm^?2 implants.     

Microstructures and properties of ultrafine-grained pure titanium processed by equal-channel angular pressing and cold deformation

Equal-channel angular pressing (ECAP) has been used to refine the grain size of commercially pure (CP) titanium as well as other metals and alloys. CP-Ti is usually processed at about 400 degrees C because it lacks sufficient ductility at lower temperature. The warm processing temperature limits the ability of the ECAP technique to improve the strength of CP-Ti. We have employed cold deformation following warm ECAP to further improve the strength of CP-Ti. Ti billets were first processed for eight passes via ECAP route Bc, with a clockwise rotation of 90 degrees between adjacent passes. The grain size obtained by ECAP alone is about 260 nm. The billets were further processed by cold deformation (cold rolling) to increase the crystalline defects such as dislocations. The strength of pure Ti was improved from 380 to around 1000 MPa by the two-step process. This article reports the microstructures, microhardness, tensile properties, and thermal stability of these Ti billets processed by a combination of ECAP and cold deformation.     

An Extensible Meta-Model for Program Analysis

Software maintenance tools for program analysis and refactoring rely on a metamodel capturing the relevant properties of programs. However, what is considered relevant may change when the tools are extended with new analyses, refactorings, and new programming languages. This paper proposes a language independent metamodel and an architecture to construct instances thereof, which is extensible for new analyses, refactorings, and new front-ends of programming languages. Due to the loose coupling between analysis, refactoring, and front-end components, new components can be added independently and reuse existing ones. Two maintenance tools implementing the metamodel and the architecture, VIZZANALYZER and X-DEVELOP, serve as proof of concept.