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PubMed2005

Quality assurance challenges in X-ray emission based analyses, the advantage of digital signal processing.

There is a large scatter in the results of X-ray analysis with solid-state detectors suggesting methodological origin. In order to improve the methodology, detector response functions have been investigated by many researchers and analysts. This was necessary as the departure of the response function of some detector-signal processing electronics from the normally assumed Gaussian line shape can exceed 100% in area. Several detector models have been proposed to improve understanding and establish a firm basis for quantitative work. After reviewing some contradictory results, we describe a signal processor that offers quality assurance, by producing two spectra for each measurement. One is the normal spectrum of accepted events, while the second spectrum contains all of the rejected events. For each measurement, therefore, all X-ray events are recorded, enabling quality control. In addition to this improvement, the digital signal processor of Cambridge Scientific, Canada, delivers a high throughput rate, excellent resolution, decreased low energy tailing and a line shape justified by the physics of the detector. Comparative measurements are presented to demonstrate the improved rejection of background from gamma rays as well as a significant improvement in pile-up recognition. The rejected events spectrum gives insight into the origin of the response function, which suggests that the flat plateau of the frequently used Hypermet function, normally attributed to detector dead layers, originates from pile-up with the low energy noise events. A detailed analysis demonstrates how the relative intensities of the X-ray lines can change in a varying noise environment, thus potentially explaining the unacceptable large scatter in the experimental data currently found in the literature. The comparison of the accepted and rejected events adds the possibility of monitoring the electronic efficiency of signal recognition that has generally been ignored in this field.

Journal Article↗

PubMed1990

Fitting density functions and diffusion tensors to three-dimensional drug transport within brain tissue.

In the research on drug transport within brain tissue, typical data sets consist of a collection of volume integrals from various locations in the brain, with each datum measuring the total amount of drug in a sample of tissue. These samples contain medically useful information about the extent and orientation of the distribution of the drug, but extracting this information requires some mathematical analysis. The method of analysis presented here performs a nonlinear regression on the data to fit a multivariate density function (for example, a Gaussian density) to model such transport processes as diffusion and dispersion. The principal components of that density then characterize the size and shape of the distribution of drug.

Algorithms↗

PubMed2005

Orientation tuning of human face processing estimated by contrast matching in transparency displays.

Upright images of faces appear more salient than faces of other orientations. We exploited this effect in a titration experiment where faces were superimposed in transparency. By manipulating the physical contrast of the component images, we measured the degree of perceptual dominance as function of the orientation of the face in the image plane. From these measurements, we obtain the orientation tuning of face processing, which is well approximated by a Gaussian function with a SD of about 45 deg and mean centered on upright. Faces predominantly lit from above and from below produced very similar results. However, when presented with scrambled faces observers showed no orientation preference. We argue that these results can be explained by the existence of specialized face processing mechanisms with an orientation tuning with a bandwidth of approximately 90 deg, predominantly centered on the upright orientation and easily disrupted by alterations of the normal facial configuration.

Contrast Sensitivity↗

PubMed2004

Calculating potentials of mean force from steered molecular dynamics simulations.

Steered molecular dynamics (SMD) permits efficient investigations of molecular processes by focusing on selected degrees of freedom. We explain how one can, in the framework of SMD, employ Jarzynski's equality (also known as the nonequilibrium work relation) to calculate potentials of mean force (PMF). We outline the theory that serves this purpose and connects nonequilibrium processes (such as SMD simulations) with equilibrium properties (such as the PMF). We review the derivation of Jarzynski's equality, generalize it to isobaric--isothermal processes, and discuss its implications in relation to the second law of thermodynamics and computer simulations. In the relevant regime of steering by means of stiff springs, we demonstrate that the work on the system is Gaussian-distributed regardless of the speed of the process simulated. In this case, the cumulant expansion of Jarzynski's equality can be safely terminated at second order. We illustrate the PMF calculation method for an exemplary simulation and demonstrate the Gaussian nature of the resulting work distribution.

Computer Simulation↗

PubMed2005

Fractional Gaussian noise, functional MRI and Alzheimer's disease.

Fractional Gaussian noise (fGn) provides a parsimonious model for stationary increments of a self-similar process parameterised by the Hurst exponent, H, and variance, sigma2. Fractional Gaussian noise with H < 0.5 demonstrates negatively autocorrelated or antipersistent behaviour; fGn with H > 0.5 demonstrates 1/f, long memory or persistent behaviour; and the special case of fGn with H = 0.5 corresponds to classical Gaussian white noise. We comparatively evaluate four possible estimators of fGn parameters, one method implemented in the time domain and three in the wavelet domain. We show that a wavelet-based maximum likelihood (ML) estimator yields the most efficient estimates of H and sigma2 in simulated fGn with 0 < H < 1. Applying this estimator to fMRI data acquired in the "resting" state from healthy young and older volunteers, we show empirically that fGn provides an accommodating model for diverse species of fMRI noise, assuming adequate preprocessing to correct effects of head movement, and that voxels with H > 0.5 tend to be concentrated in cortex whereas voxels with H < 0.5 are more frequently located in ventricles and sulcal CSF. The wavelet-ML estimator can be generalised to estimate the parameter vector beta for general linear modelling (GLM) of a physiological response to experimental stimulation and we demonstrate nominal type I error control in multiple testing of beta, divided by its standard error, in simulated and biological data under the null hypothesis beta = 0. We illustrate these methods principally by showing that there are significant differences between patients with early Alzheimer's disease (AD) and age-matched comparison subjects in the persistence of fGn in the medial and lateral temporal lobes, insula, dorsal cingulate/medial premotor cortex, and left pre- and postcentral gyrus: patients with AD had greater persistence of resting fMRI noise (larger H) in these regions. Comparable abnormalities in the AD patients were also identified by a permutation test of local differences in the first-order autoregression AR(1) coefficient, which was significantly more positive in patients. However, we found that the Hurst exponent provided a more sensitive metric than the AR(1) coefficient to detect these differences, perhaps because neurophysiological changes in early AD are naturally better described in terms of abnormal salience of long memory dynamics than a change in the strength of association between immediately consecutive time points. We conclude that parsimonious mapping of fMRI noise properties in terms of fGn parameters efficiently estimated in the wavelet domain is feasible and can enhance insight into the pathophysiology of Alzheimer's disease.

Aged↗

PubMed1997

A computerized analysis system in chest radiography: evaluation of interstitial lung abnormalities.

We evaluated the usefulness of a computerized analysis system in the detection of interstitial lung abnormalities in digitized chest radiography. This system uses the processes of four-directional Laplacian-Gaussian filtering, linear opacity judgment, and linear opacity subtraction. For qualitative analysis, we employed a combined radiographic index, which was calculated from two normalized radiographic indices obtained by linear opacity judgment and subtraction of linear opacities. We selected 50 regions of interest (ROIs) in patients with mild interstitial lung abnormalities, 50 ROIs in patients with severe interstitial lung abnormalities, and 50 ROIs in individuals with normal lung parenchyma. High-resolution computed radiography (HRCT) findings were used as the standard of reference for this study. These ROIs were processed by our computerized analysis system, and radiographic indices were obtained from each ROI. The area under the receiver operating characteristic curve (Az) was used as the measure of performance. The combined radiographic index provided better results in the mild interstitial lung abnormality group (Az = 0.94 +/- 0.02), but it also yielded good results in the severe interstitial lung abnormality group (Az = 0.98 +/- 0.01). These results indicate that this system of combining radiographic indices has improved the detection performance over that with our previous system.

Evaluation Studies as Topic↗

PubMed2002

Learning generative models of natural images.

This work proposes an unsupervised learning process for analysis of natural images. The derivation is based on a generative model, a stochastic coin-flip process directly operating on many disjoint multivariate Gaussian distributions. Following the maximal likelihood principle and using the Potts encoding, the goodness-of-fit of the generative model to tremendous patches randomly sampled from natural images is quantitatively expressed by an objective function subject to a set of constraints. By further combination of the objective function and the minimal wiring criterion, we achieve a mixed integer and linear programming. A hybrid of the mean field annealing and the gradient descent method is applied to the mathematical framework and produces three sets of interactive dynamics for the learning process. Numerical simulations show that the learning process is effective for extraction of orientation, localization and bandpass features and the generative model can make an ensemble of a sparse code for natural images.

Learning↗

PubMed2004

Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN).

Type-2 fuzzy logic system (FLS) cascaded with neural network, type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part, and the two-layer interval neural network as the consequent part. A general T2FNN is computational-intensive due to the complexity of type 2 to type 1 reduction. Therefore, the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates cannot be both negative. Further, due to variation of the initial MF parameters, i.e., the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search optimal spread rate for uncertain means and optimal learning for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.

Algorithms↗

PubMed2003

Deprotonation sites of acetohydroxamic acid isomers. A theoretical and experimental study.

Theoretical (ab initio calculations) and experimental (NMR, spectrophotometric, and potentiometric measurements) investigations of the isomers of acetohydroxamic acid (AHA) and their deprotonation processes have been performed. Calculations with the Gaussian 98 package, refined at the MP2(FC)/AUG-cc-pVDZ level considering the molecule isolated, indicate that the Z(cis) amide is the most stable form of the neutral molecule. This species and the less stable (Z)-imide form undergo deprotonation, giving rise to two stable anions. Upon deprotonation, the E(trans) forms give three stable anions. The ab initio calculations were performed in solution as well, regarding water as a continuous dielectric; on the basis of the relative energies of the most stable anion and neutral forms, calculated with MP2/PCM/AUG-cc-pVDZ, N-deprotonation of the amide (Z or E) structure appeared to be the most likely process in solution. NMR measurements provided evidence for the existence of (Z)- and (E)-isomers of both the neutral and anion forms in solution. Comparisons of the dynamic NMR and NOESY (one-dimensional) results obtained for the neutral species and their anions were consistent with N-deprotonation, which occurred preferentially to O-deprotonation. The (microscopic) acid dissociation constants of the two isomers determined at 25 degrees C from the pH dependence of the relevant chemical shifts, pK(E) = 9.01 and pK(Z) = 9.35, were consistent with the spectrophotometric and potentiometric evaluations (pK(HA) = 9.31).

Journal Article↗

PubMed1998

Comparison of frequency-distance relationship and Gaussian-diffusion-based methods of compensation for distance-dependent spatial resolution in SPECT imaging.

The goal of this investigation was to compare resolution recovery versus noise level of two methods for compensation of distance-dependent resolution (DDR) in SPECT imaging. The two methods of compensation were restoration filtering based on the frequency-distance relationship (FDR) prior to iterative reconstruction, and modelling DDR in the projector/backprojector pair employed in iterative reconstruction. FDR restoration filtering was computationally faster than modelling the detector response in iterative reconstruction. Using Gaussian diffusion to model the detector response in iterative reconstruction sped up the process by a factor of 2.5 over frequency domain filtering in the projector/backprojector pair. Gaussian diffusion modelling resulted in a better resolution versus noise tradeoff than either FDR restoration filtering or solely modelling attenuation in the projector/backprojector pair of iterative reconstruction. For the pixel size investigated herein (0.317 cm), accounting for DDR in the projector/backprojector pair by Gaussian diffusion, or by applying a blurring function based on the distance from the face of the collimator at each distance, resulted in very similar resolution recovery and slice noise level.

Computer Simulation↗

PubMedDate not supplied

On some computational results for single neurons' activity modeling.

The classical Ornstein-Uhlenbeck diffusion neuronal model is generalized by inclusion of a time-dependent input whose strength exponentially decreases in time. The behavior of the membrane potential is consequently seen to be modeled by a process whose mean and covariance classify, it as Gaussian-Markov. The effect of the input on the neuron's firing characteristics is investigated by comparing the firing probability densities and distributions for such a process with the corresponding ones of the Ornstein-Uhlenbeck model. All numerical results are obtained by implementation of a recently developed computational method.

Computational Biology↗

PubMed2002

Unsupervised continuous sleep analysis.

One aim of the EU-funded project SIESTA was to develop a new way of describing the human sleep-wake continuum with high temporal resolution, and independent of subjective rules, to serve as an alternative to traditional sleep scoring. Here, we report new findings obtained with a fully automatic, probabilistic sleep-analyzer using Hidden Markov Models (HMMs) based on data from a single electroencephalogram (EEG) channel. HMMs allow the analysis of non-stationary time series by modeling both the probability density functions of locally stationary data and the transition probabilities between these stable states. In the context of sleep analysis, the locally stable states can be thought of as sleep stages. The sleep-wake continuum was modeled as a mixture of three different processes by defining a three-state Gaussian Observation HMM (GOHMM). No class information from human scorers was used to train the model. The probabilities of being in any of the three states at each point in time roughly indicate the amount of wakefulness, deep sleep and rapid-eye-movement (REM) sleep with a one-second time resolution. Although it was not the aim to replicate the traditional Rechtschaffen and Kales (R&K) scoring, pseudo R&K hypnograms were constructed from the probability plots in order to compare the analyzer results with classical sleep stages by human experts. We expected that the analyzer would be able to classify data correctly from the "cornerstones" of human sleep (wakefulness, deep sleep, and REM sleep). Contrary to our previous efforts, we trained the HMMs on data from two different sleep laboratories separately, instead of generalizing data from diverse laboratories. While these stages could be detected with an accuracy of around 80% at the sleep laboratory for which we already had achieved the best results, there was no improvement from previous results by the training of a separate model in the other laboratory. This finding indicates clear laboratory effects in the signal characteristics, probably due to differences in hardware and filter settings. The presented approach, going beyond a mere replication of the traditional R&K standard, offers a continuous description of human sleep which is based on probabilistic principles. It provides a second-by-second quantification of the sleep-wake continuum and captures, although being entirely data-driven instead of rule-based, the three main processes in human sleep: wakefulness, deep sleep and REM sleep.

Adult↗

PubMed1999

Harmonic potential driven by long-range correlated noise.

The probability distribution of a particle in a quadratic potential driven by Gaussian long-range correlated noise has been obtained. The long-time asymptotic relaxation of the stochastic process has been characterized in terms of the long-range correlated noise appearing in the corresponding stochastic differential equation defining the process. The particular case when the particle is driven by a Gaussian color noise has also been revisited, in this way giving its exact probability distribution for all time. By using a characteristic functional technique reported previously, results for a non-Gaussian long-range correlated noise are also shown. Emphasis has been placed on solving the plane rotator in the presence of arbitrary random torques having long-range correlations. In order to show strong and weak non-Markovian effects coming from different sources of noise, the undamped free particle under the influence of arbitrary accelerations has been analyzed. We also analyze in detail the structure of the trajectories of the overdamped and undamped free particle.

Journal Article↗

PubMed2004

Application of multiway ICA for on-line process monitoring of a sequencing batch reactor.

Multiway principal component analysis has been shown to be a powerful monitoring tool in many industrial batch processes. However, it has the shortcomings that all batch lengths should be equal, the measurement variables must be normally distributed and that future values of the current batch must be estimated to allow on-line monitoring. In this work, it is shown that multiway independent component analysis (MICA) can be used to overcome these drawbacks and obtain better monitoring performance. The on-line MICA monitoring of batch processes is based on a new unfolding method and independent component analysis (ICA). ICA provides better monitoring performance than PCA in cases with non-Gaussian data because it is not based on the assumption that the latent variables are normally distributed. The MICA algorithm does not require any estimation of future batch values and can also be applied to non-equal batch length data sets. This article describes the application of on-line MICA monitoring of a sequencing batch reactor (SBR). It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of disturbance sources with non-Gaussian characteristics. The SBR poses an interesting challenge from the point of process monitoring characterized by non-stationary, batchwise, multiscale, and non-Gaussian characteristics. The results of the bench-scale SBR monitoring clearly showed the power and advantages of MICA monitoring in comparison to conventional monitoring methods.

Automation↗

PubMed1992

Steady-state kinetics of solitary batrachotoxin-treated sodium channels. Kinetics on a bounded continuum of polymer conformations.

The underlying principles of the kinetics and equilibrium of a solitary sodium channel in the steady state are examined. Both the open and closed kinetics are postulated to result from round-trip excursions from a transition region that separates the openable and closed forms. Exponential behavior of the kinetics can have origins different from small-molecule systems. These differences suggest that the probability density functions (PDFs) that describe the time dependences of the open and closed forms arise from a distribution of rate constants. The distribution is likely to arise from a thermal modulation of the channel structure, and this provides a physical basis for the following three-variable equation: [formula; see text] Here, A0 is a scaling term, k is the mean rate constant, and sigma quantifies the Gaussian spread for the contributions of a range of effective rate constants. The maximum contribution is made by k, with rates faster and slower contributing less. (When sigma, the standard deviation of the spread, goes to zero, then p(f) = A0 e-kt.) The equation is applied to the single-channel steady-state probability density functions for batrachotoxin-treated sodium channels (1986. Keller et al. J. Gen. Physiol. 88: 1-23). The following characteristics are found: (a) The data for both open and closed forms of the channel are fit well with the above equation, which represents a Gaussian distribution of first-order rate processes. (b) The simple relationship [formula; see text] holds for the mean effective rat constants. Or, equivalently stated, the values of P open calculated from the k values closely agree with the P open values found directly from the PDF data. (c) In agreement with the known behavior of voltage-dependent rate constants, the voltage dependences of the mean effective rate constants for the opening and closing of the channel are equal and opposite over the voltage range studied. That is, [formula; see text] "Bursts" are related to the well-known cage effect of solution chemistry.

Batrachotoxins↗

PubMed2026

Dual-contrastive learning for spatial domain identification in spatial transcriptomics with STAMGC.

Spatial transcriptomics (STs) have become a valuable approach for understanding the growth and development of organisms. Despite the recent emergence of numerous ST models, accurately identifying spatial domains remains challenging owing to the trade-off between preserving local details and reducing noise. Here, we introduce STAMGC, which is a dual-contrastive learning framework built upon graph convolutional networks. This model leverages regional and topological contrastive learning to jointly optimize the model, effectively reducing the noise in spatial domain identification and enhancing the extraction of detailed features. In this study, Gaussian smoothing, originally developed in the image processing field, is introduced to process ST data, providing a foundation for region-level contrastive learning by mitigating spatial discontinuities of gene expression signals. Experimental results indicate that STAMGC outperforms existing methods across multiple data sets according to comprehensive evaluations. Furthermore, STAMGC not only identifies finer structures in the mouse brain but also brings new discoveries for human breast cancer research.

Journal Article↗

PubMed2006

State-dependent fire models and related renewal processes.

We introduce a general class of stochastic processes forced by instantaneous random fires (i.e., jumps) that reset the state variable x to a given value. Since in many physical systems the fire activity is often dependent on the actual value of the state variable, as in the case of natural fires in ecosystems and firing dynamics in neuronal activity, the frequency of fire occurrence is assumed to be state dependent. Such dynamics leads to independent interfire statistics--i.e., to renewal point processes. Various functions relating the frequency of fire occurrence to x(t) are analyzed and compared. The relation between the probabilistic dynamics of x(t) and the interfire statistics is derived and some exact probability distribution of both x(t) and the interfire times are obtained for systems with different degrees of complexity. After studying processes in which the fire activity is coupled only to a deterministic drift, we also analyze processes forced by either additive or multiplicative Gaussian white noise.

Journal Article↗

PubMed2005

Fractal noises and motions in time series of presympathetic and sympathetic neural activities.

We used Allan factor analysis to classify time series of the discharges of single presympathetic neurons in the cat medullary lateral tegmental field (LTF) and rostral ventrolateral medulla (RVLM) and of the postganglionic vertebral sympathetic nerve. These time series fell into two classes of fractal-based point processes characterized by statistically self-similar behavior reflecting long-range correlations among data points. Classification of a time series as either a fractional Gaussian noise (fGn)-or fractional Brownian motion (fBm)-based point process depended on the scaling exponent, alpha, of the power law in the Allan factor curve. fGn is defined as 0 < alpha < 1 and fBm as 1 < alpha < 3. The process responsible for the fractal spike trains of 11 of 12 classifiable LTF neurons with sympathetic nerve-related activity was fGn. In contrast, the process responsible for the fractal spike trains of eight of nine classifiable RVLM presympathetic neurons was fBm. The time series of simultaneously recorded vertebral sympathetic nerve discharge and the arterial pulse also were fBm-based signals. Because a fBm signal is the cumulative sum of the elements comprising the corresponding fGn signal, these results show smoothing of fractal time series in a feedforward direction from medullary presympathetic neurons to postganglionic sympathetic neurons. This may involve integration by RVLM neurons of their LTF inputs or independent fractal processes acting at different levels of the network controlling sympathetic nerve discharge. Whether feedforward smoothing of fractal signals is a feature in other neural systems is open to investigation.

Action Potentials↗