Detection of Parkinson’s disease tremor-onset with a frequency analysing artificial neural network

Deep Brain Stimulator devices are becoming widely used for therapeutic benefits in movement disorders such as Parkinson's disease. Prolonging the battery life span of such devices could dramatically reduce the risks and accumulative costs associated with surgical replacement. This paper demonstrates how an artificial neural network can be trained using pre-processing frequency analysis of deep brain electrode recordings to detect the onset of tremor in Parkinsonian patients. Implementing this solution into an 'intelligent' neurostimulator device will remove the need for continuous stimulation currently used, and open up the possibility of demand-driven stimulation. Such a methodology could potentially decrease the power consumption of a deep brain pulse generator.

Resonance response of electrorheological fluids in vertical oscillation squeeze flow

The resonance effect of microcrystalline cellulose/castor oil electrorheological (ER) suspensions was studied in a compressed oscillatory squeeze flow under external electric fields. The resonance frequency first increases linearly with increasing external held, and then shift to high-field plateau. The amplitudes of resonance peak increase sharply with the applied fields in the range of 0.17-1.67kV/mm. The phase difference of the reduced displacement relative to the excitation force inverses in the case of resonance. A viscoelasticity model of the ER suspensions, which offers both the equivalent stiffness and the viscous damping, should be responsible for the appearance of resonance. The influence of the electric field on the resonance frequency and the resonance hump is consistent qualitatively with the interpretation of our proposed model. Storage modulus G' was presented for the purpose of investigating this influence.

Individual variation in psychological and psychomotor symptoms following smoking cessation: the implications for treatment

Some commonly experienced signs and symptoms occur during abstinence from tobacco, but specific signs and symptoms and their intensity vary greatly from individual to individual. The aim of this study was to re-examine psychological and psychomotor symptoms in smokers in the general population, and to explore the individual variation in these. Quitting smokers (n = 123) reported their experiences pre- and post-cessation, on a questionnaire developed for the study. Analysis of variance and frequency analysis showed significant decreases between pre- and post-cessation on positive experiences (F = 9.81, p < 0.0001) but no significant change on negative experiences, suggesting a loss of pleasure rather than increased negative affect upon quitting. The variance of the pre- to post-cessation difference score suggested wide variation in the reporting of withdrawal symptoms. These results lead us to consider the implications for treatment, using cognitive therapies and moderating the significant emphasis that is at present put on withdrawal.

Constitutive oligomerization of human D-2 dopamine receptors expressed in Spodoptera frugiperda 9 (Sf9) and in HEK293 cells - Analysis using co-immunoprecipitation and time-resolved fluorescence resonance energy transfer

Human D-2Long (D-2L) and D-2Short (D-2S) dopamine receptor isoforms were modified at their N-terminus by the addition of a human immunodeficiency virus (HIV) or a FLAG epitope tag. The receptors were then expressed in Spodoptera frugiperda 9 (Sf9) cells using the baculovirus system, and their oligomerization was investigated by means of co-immunoprecipitation and time-resolved fluorescence resonance energy transfer (FRET). [H-3] Spiperone labelled D-2 receptors in membranes prepared from Sf9 cells expressing epitope-tagged D-2L or D-2S receptors, with a pK(d) value of approximate to 10. Co-immunoprecipitation using antibodies specific for the tags showed constitutive homo-oligomerization of D-2L and D-2S receptors in Sf9 cells. When the FLAG-tagged D-2S and HIV-tagged D-2L receptors were co-expressed, co-immunoprecipitation showed that the two isoforms can also form hetero-oligomers in Sf9 cells. Time-resolved FRET with europium and XL665-labelled antibodies was applied to whole Sf9 cells and to membranes from Sf9 cells expressing epitope-tagged D-2 receptors. In both cases, constitutive homo-oligomers were revealed for D-2L and D-2S isoforms. Time-resolved FRET also revealed constitutive homo-oligomers in HEK293 cells expressing FLAG-tagged D-2S receptors. The D-2 receptor ligands dopamine, R-(-) propylnorapomorphine, and raclopride did not affect oligomerization of D-2L and D-2S in Sf9 and HEK293 cells. Human D-2 dopamine receptors can therefore form constitutive oligomers in Sf9 cells and in HEK293 cells that can be detected by different approaches, and D-2 oligomerization in these cells is not regulated by ligands.

Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis

We present the symbolic resonance analysis (SRA) as a viable method for addressing the problem of enhancing a weakly dominant mode in a mixture of impulse responses obtained from a nonlinear dynamical system. We demonstrate this using results from a numerical simulation with Duffing oscillators in different domains of their parameter space, and by analyzing event-related brain potentials (ERPs) from a language processing experiment in German as a representative application. In this paradigm, the averaged ERPs exhibit an N400 followed by a sentence final negativity. Contemporary sentence processing models predict a late positivity (P600) as well. We show that the SRA is able to unveil the P600 evoked by the critical stimuli as a weakly dominant mode from the covering sentence final negativity. (c) 2007 American Institute of Physics. (c) 2007 American Institute of Physics.

An analysis of real time implementation of Fourier transform-based frequency recognition algorithms

Frequency recognition is an important task in many engineering fields such as audio signal processing and telecommunications engineering, for example in applications like Dual-Tone Multi-Frequency (DTMF) detection or the recognition of the carrier frequency of a Global Positioning, System (GPS) signal. This paper will present results of investigations on several common Fourier Transform-based frequency recognition algorithms implemented in real time on a Texas Instruments (TI) TMS320C6713 Digital Signal Processor (DSP) core. In addition, suitable metrics are going to be evaluated in order to ascertain which of these selected algorithms is appropriate for audio signal processing(1).

Multivariate analysis of the dielectric response of materials modeled using networks of resistors and capacitors

We discuss the modeling of dielectric responses of electromagnetically excited networks which are composed of a mixture of capacitors and resistors. Such networks can be employed as lumped-parameter circuits to model the response of composite materials containing conductive and insulating grains. The dynamics of the excited network systems are studied using a state space model derived from a randomized incidence matrix. Time and frequency domain responses from synthetic data sets generated from state space models are analyzed for the purpose of estimating the fraction of capacitors in the network. Good results were obtained by using either the time-domain response to a pulse excitation or impedance data at selected frequencies. A chemometric framework based on a Successive Projections Algorithm (SPA) enables the construction of multiple linear regression (MLR) models which can efficiently determine the ratio of conductive to insulating components in composite material samples. The proposed method avoids restrictions commonly associated with Archie’s law, the application of percolation theory or Kohlrausch-Williams-Watts models and is applicable to experimental results generated by either time domain transient spectrometers or continuous-wave instruments. Furthermore, it is quite generic and applicable to tomography, acoustics as well as other spectroscopies such as nuclear magnetic resonance, electron paramagnetic resonance and, therefore, should be of general interest across the dielectrics community.

Analysis of frequency and intensity of European winter storm events from a multi-model perspective, at synoptic and regional scales

This study focuses on the analysis of winter (October-November-December-January-February-March; ONDJFM) storm events and their changes due to increased anthropogenic greenhouse gas concentrations over Europe. In order to assess uncertainties that are due to model formulation, 4 regional climate models (RCMs) with 5 high resolution experiments, and 4 global general circulation models (GCMs) are considered. Firstly, cyclone systems as synoptic scale processes in winter are investigated, as they are a principal cause of the occurrence of extreme, damage-causing wind speeds. This is achieved by use of an objective cyclone identification and tracking algorithm applied to GCMs. Secondly, changes in extreme near-surface wind speeds are analysed. Based on percentile thresholds, the studied extreme wind speed indices allow a consistent analysis over Europe that takes systematic deviations of the models into account. Relative changes in both intensity and frequency of extreme winds and their related uncertainties are assessed and related to changing patterns of extreme cyclones. A common feature of all investigated GCMs is a reduced track density over central Europe under climate change conditions, if all systems are considered. If only extreme (i.e. the strongest 5%) cyclones are taken into account, an increasing cyclone activity for western parts of central Europe is apparent; however, the climate change signal reveals a reduced spatial coherency when compared to all systems, which exposes partially contrary results. With respect to extreme wind speeds, significant positive changes in intensity and frequency are obtained over at least 3 and 20% of the European domain under study (35–72°N and 15°W–43°E), respectively. Location and extension of the affected areas (up to 60 and 50% of the domain for intensity and frequency, respectively), as well as levels of changes (up to +15 and +200% for intensity and frequency, respectively) are shown to be highly dependent on the driving GCM, whereas differences between RCMs when driven by the same GCM are relatively small.

Analysis of diffusion tensor magnetic resonance imaging data using principal component analysis

An analysis method for diffusion tensor (DT) magnetic resonance imaging data is described, which, contrary to the standard method (multivariate fitting), does not require a specific functional model for diffusion-weighted (DW) signals. The method uses principal component analysis (PCA) under the assumption of a single fibre per pixel. PCA and the standard method were compared using simulations and human brain data. The two methods were equivalent in determining fibre orientation. PCA-derived fractional anisotropy and DT relative anisotropy had similar signal-to-noise ratio (SNR) and dependence on fibre shape. PCA-derived mean diffusivity had similar SNR to the respective DT scalar, and it depended on fibre anisotropy. Appropriate scaling of the PCA measures resulted in very good agreement between PCA and DT maps. In conclusion, the assumption of a specific functional model for DW signals is not necessary for characterization of anisotropic diffusion in a single fibre.