Detection of jitter in intertarget spacing by the big brown bat Eptesicus fuscus

We trained bats to detect intertarget jitter, i.e., relative motion between two virtual (electronically synthesized) targets. Both targets were themselves moving with respect to nearby objects (e.g., the microphone and speaker used to create the virtual targets) so that the only reliable cue available to the bats was variation in intertarget spacing. Given a target at 80 cm and another at 95, 110 or 125 cm, the threshold for intertarget jitter (ITJ) of the two bats tested was <10 μs, corresponding to <1.7 mm of range. When, for one bat, we increased the range instability of the targets by adding varying amounts of random range shift to the target complex (while preserving the correct intertarget spacing), ITJ threshold worsened. When we presented three targets, one of which was jittering, the bat's threshold improved to 0.9 μs (equivalent to 0.16 mm). If no second target was presented, i.e., if the task was to detect jitter added to a single moving target, then bats' jitter threshold was very high (>200 μs). Eptesicus fuscus appears to be very good at detecting changes in intertarget spacing, which might prove valuable for detecting targets moving relative to the background or for constructing a spatial image of a complex environment. Accepted: 7 April 1997     

Stochastic mechanical model of vocal folds for producing jitter and for identifying pathologies through real voices

Jitter, in voice production applications, is a random phenomenon characterized by the deviation of the glottal cycle length with respect to a mean value. Its study can help in identifying pathologies related to the vocal folds according to the values obtained through the different ways to measure it. This paper aims to propose a stochastic model, considering three control parameters, to generate jitter based on a deterministic one-mass model for the dynamics of the vocal folds and to identify parameters from the stochastic model taking into account real voice signals experimentally obtained. To solve the corresponding stochastic inverse problem, the cost function used is based on the distance between probability density functions of the random variables associated with the fundamental frequencies obtained by the experimental voices and the simulated ones, and also on the distance between features extracted from the voice signals, simulated and experimental, to calculate jitter. The results obtained show that the model proposed is valid and some samples of voices are synthesized considering the identified parameters for normal and pathological cases. The strategy adopted is also a novelty and mainly because a solution was obtained. In addition to the use of three parameters to construct the model of jitter, it is the discussion of a parameter related to the bandwidth of the power spectral density function of the stochastic process to measure the quality of the signal generated. A study about the influence of all the main parameters is also performed. The identification of the parameters of the model considering pathological cases is maybe of all novelties introduced by the paper the most interesting.     

The transfer function of a target limits the jitter detection threshold with signals of echolocating FM-bats

The delay jitter discrimination threshold in bats is a disputed subject. Some investigators have obtained results indicating that bats are able to discriminate alternations in delay down to 10 ns, which appears incredible for purely physical reasons. Using actual bat echolocation sequences recorded during an easy detection task to measure simulated delay jitter, it is shown here that jitter detection thresholds in the order of some tens of nanoseconds are actually physically realizable. However, if the transfer function of the target simulating apparatus is not perfect, the lowest thresholds are in the order of hundreds of nanoseconds and variable between individual bats. This phenomenon is shown to arise as a consequence of the variation in signal parameters from call to call. When the transfer function from a real jitter experiment was artificially applied to the echoes, the jitter detection thresholds again were several hundred nanoseconds. This is the first study to point out a limiting role of the transfer function of a system faced with variations in echolocation signal parameters, something that should be considered in evaluating all sonar systems with variable signal structure.     

Dimensionality reduction for visualization of normal and pathological speech data

For an adequate analysis of pathological speech signals, a sizeable number of parameters is required, such as those related to jitter, shimmer and noise content. Often this kind of high-dimensional signal representation is difficult to understand, even for expert voice therapists and physicians. Data visualization of a high-dimensional dataset can provide a useful first step in its exploratory data analysis, facilitating an understanding about its underlying structure. In the present paper, eight dimensionality reduction techniques, both classical and recent, are compared on speech data containing normal and pathological speech. A qualitative analysis of their dimensionality reduction capabilities is presented. The transformed data are also quantitatively evaluated, using classifiers, and it is found that it may be advantageous to perform the classification process on the transformed data, rather than on the original. These qualitative and quantitative analyses allow us to conclude that a nonlinear, supervised method, called kernel local Fisher discriminant analysis is superior for dimensionality reduction in the actual context.     

Mathematical modeling and parameter estimation of axonal cargo transport

This paper is about how cortical recurrent interactions in primary visual cortex (V1) together with feedback from extrastriate cortex can account for spectral peaks in the V1 local field potential (LFP). Recent studies showed that visual stimulation enhances the γ-band (25–90 Hz) of the LFP power spectrum in macaque V1. The height and location of the γ-band peak in the LFP spectrum were correlated with visual stimulus size. Extensive spatial summation, possibly mediated by feedback connections from extrastriate cortex and long-range horizontal connections in V1, must play a crucial role in the size dependence of the LFP. To analyze stimulus-effects on the LFP of V1 cortex, we propose a network model for the visual cortex that includes two populations of V1 neurons, excitatory and inhibitory, and also includes feedback to V1 from extrastriate cortex. The neural network model for V1 was a resonant system. The model’s resonance frequency (ResF) was in the γ-band and varied up or down in frequency depending on cortical feedback. The model’s ResF shifted downward with stimulus size, as in the real cortex, because increased size recruited more activity in extrastriate cortex and V1 thereby causing stronger feedback. The model needed to have strong local recurrent inhibition within V1 to obtain ResFs that agree with cortical data. Network resonance as a consequence of recurrent excitation and inhibition appears to be a likely explanation for γ-band peaks in the LFP power spectrum of the primary visual cortex.     

Mathematical modeling and parameters estimation of anaerobic fermentation processes

In the paper some problems of the mathematical modeling of anaerobic (methane) fermentation of animal waste in stirred tank bioreactors are considered. Laboratory experiments are carried out with highly concentrated organic pollutants and transient step responses of the control output for continuous methane fermentation are obtained. The dynamic behavior of this process is described by sets of deterministic nonlinear differential equations from 2nd order with different structures. Static characteristics are obtained with these models analytically. Investigations by computer simulation of the methane fermentation are performed with the aim to chose appropriate models for automatic control system design of this process.     

Individual based modeling and parameter estimation for a Lotka-Volterra system

Stochastic component, inevitable in biological systems, makes problematic the estimation of the model parameters from a single sequence of measurements, despite the complete knowledge of the system. We studied the problem of parameter estimation using individual-based computer simulations of a 'Lotka-Volterra world'. Two kinds (species) of particles--X (preys) and Y (predators)--moved on a sphere according to deterministic rules and at the collision (interaction) of X and Y the particle X was changed to a new particle Y. Birth of preys and death of predators were simulated by addition of X and removal of Y, respectively, according to exponential probability distributions. With this arrangement of the system, the numbers of particles of each kind might be described by the Lotka-Volterra equations. The simulations of the system with low (200-400 particles on average) number of individuals showed unstable oscillations of the population size. In some simulation runs one of the species became extinct. Nevertheless, the oscillations had some generic properties (e.g. mean, in one simulation run, oscillation period, mean ratio of the amplitudes of the consecutive maxima of X and Y numbers, etc.) characteristic for the solutions of the Lotka-Volterra equations. This observation made it possible to estimate the four parameters of the Lotka-Volterra model with high accuracy and good precision. The estimation was performed using the integral form of the Lotka-Volterra equations and two parameter linear regression for each oscillation cycle separately. We conclude that in spite of the irregular time course of the number of individuals in each population due to stochastic intraspecies component, the generic features of the simulated system evolution can provide enough information for quantitative estimation of the system parameters.     

Frequency response of the chest: modeling and parameter estimation.

The frequency response of the respiratory system was studied in the range from 3 to 70 Hz in 15 normal subjects by applying sinusoidal pressure variations around the chest and measuring gas flow at the mouth. The observed input-output relationships were systematically compared to those predicted on the basis of linear differential equations of increasing order. From 3 to 20 Hz the behavior of the system was best described by a 3rd-order equation, and from 3 to 50 Hz by a 4th-order one. A mechanistic model of the 4th order, featuring tissue compliance (Ct), resistance (Rt) and inertance (It), alveolar gas compressibility (Cg) and airway resistance (Raw), and inertance (Iaw) was developed. Using that model, the following mean values were found: Ct = 2.08-10(-2)1-hPa-1 (1 hPa congruent to 1 cm of water); Rt = 1.10-hPa-1(-1)-s; It = 0.21-10(-2)hPa-1(-1)-s2; Raw = 1.35-hPa-1(-1)-s; Iaw = 2.55-10(-2)hPa-1(-1)-s2. Additional experiments devised to validate the model were reasonably successful, suggesting that the physical meaning attributed to the coefficients was correct. The validity of the assumptions and the physiological meaning of the coefficients are discussed.     

HAPLOPOOL: improving haplotype frequency estimation through DNA pools and phylogenetic modeling

MOTIVATION: The search for genetic variants that are linked to complex diseases such as cancer, Parkinson's;, or Alzheimer's; disease, may lead to better treatments. Since haplotypes can serve as proxies for hidden variants, one method of finding the linked variants is to look for case-control associations between the haplotypes and disease. Finding these associations requires a high-quality estimation of the haplotype frequencies in the population. To this end, we present, HaploPool, a method of estimating haplotype frequencies from blocks of consecutive SNPs. RESULTS: HaploPool leverages the efficiency of DNA pools and estimates the population haplotype frequencies from pools of disjoint sets, each containing two or three unrelated individuals. We study the trade-off between pooling efficiency and accuracy of haplotype frequency estimates. For a fixed genotyping budget, HaploPool performs favorably on pools of two individuals as compared with a state-of-the-art non-pooled phasing method, PHASE. Of independent interest, HaploPool can be used to phase non-pooled genotype data with an accuracy approaching that of PHASE. We compared our algorithm to three programs that estimate haplotype frequencies from pooled data. HaploPool is an order of magnitude more efficient (at least six times faster), and considerably more accurate than previous methods. In contrast to previous methods, HaploPool performs well with missing data, genotyping errors and long haplotype blocks (of between 5 and 25 SNPs).     

Stochastic compartmental modeling and parameter estimation with application to cancer treatment follow-up studies

In this paper we use marginal probabilities to derive expressions for the means, variances and covariances ofm-compartment systems. We also present an efficient algorithm for the estimation of the parameters of the system using time series data when measurements are available fromk of them compartments. An application of the analysis and parameter estimation procedure for a model representing the results of a cancer treatment follow-up study is given. Supported in part by NSF Grant Number DCR74-17282.     

Robust modeling in screening studies: estimation of sensitivity and preclinical sojourn time distribution

In early-detection clinical trials, quantities such as the sensitivity of the screening modality and the preclinical duration of the disease are important to describe the natural history of the disease and its interaction with a screening program. Assume that the schedule of a screening program is periodic and that the sojourn time in the preclinical state has a piecewise density function. Modeling the preclinical sojourn time distribution as a piecewise density function results in robust estimation of the distribution function. Our aim is to estimate the piecewise density function and the examination sensitivity using both generalized least squares and maximum likelihood methods. We carried out extensive simulations to evaluate the performance of the methods of estimation. The different estimation methods provide complimentary tools to obtain the unknown parameters. The methods are applied to three breast cancer early-detection trials.     

Assessment of reliability of microarray data and estimation of signal thresholds using mixture modeling

DNA microarray is an important tool for the study of gene activities but the resultant data consisting of thousands of points are error-prone. A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. In this study, we describe an approach based on normal mixture modeling for determining optimal signal intensity thresholds to identify reliable measurements of the microarray elements and subsequently eliminate false expression ratios. We used univariate and bivariate mixture modeling to segregate the microarray data into two classes, low signal intensity and reliable signal intensity populations, and applied Bayesian decision theory to find the optimal signal thresholds. The bivariate analysis approach was found to be more accurate than the univariate approach; both approaches were superior to a conventional method when validated against a reference set of biological data that consisted of true and false gene expression data. Elimination of unreliable signal intensities in microarray data should contribute to the quality of microarray data including reproducibility and reliability of gene expression ratios.     

On the kinematic modeling and the parameter estimation of the human knee joint.

This paper presents some results on the modeling and the parameter estimation of the human knee joint. Based on the geometric characteristics of the femur condyle and the tibia plateau, a part of femoro-tibial joint model includes an involute-on-plane submodel. Data recorded by camera type device are used to analyze the kinematic characteristics of the knee joint and to estimate the corresponding submodel parameters. Experimental results are presented and the model is further validated.     

GPS receivers timing data processing using neural networks: optimal estimation and errors modeling

The Global Positioning System (GPS) is a network of satellites, whose original purpose was to provide accurate navigation, guidance, and time transfer to military users. The past decade has also seen rapid concurrent growth in civilian GPS applications, including farming, mining, surveying, marine, and outdoor recreation. One of the most significant of these civilian applications is commercial aviation. A stand-alone civilian user enjoys an accuracy of 100 meters and 300 nanoseconds, 25 meters and 200 nanoseconds, before and after Selective Availability (SA) was turned off. In some applications, high accuracy is required. In this paper, five Neural Networks (NNs) are proposed for acceptable noise reduction of GPS receivers timing data. The paper uses from an actual data collection for evaluating the performance of the methods. An experimental test setup is designed and implemented for this purpose. The obtained experimental results from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of methods to give high accurate timing. Quality of the obtained results is very good, so that GPS timing RMS error reduce to less than 120 and 40 nanoseconds, with and without SA.