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At least 235 records · Page 13Linked to original sources

Optimal radiation beam profiles considering uncertainties in beam patient alignment.

The often large uncertainties that exist in beam patient alignment during radiation therapy may require modification of the incident beams to ensure an optimal delivered dose distribution to the target volume. This problem becomes increasingly severe when the required dose distribution of the incident beams becomes more heterogeneous. A simple analytical formula is derived for the case when the fraction number is high, and the desired relative dose variations are small. This formula adjusts the fluence distribution of the incident beam so that the resultant dose distribution will be as close as possible to the desired one considering the uncertainties in beam patient alignment. When sharp dose gradients are important, for instance at the border of the target volume, the problem is much more difficult. It is shown here that, if the tumor is surrounded by organs at risk, it is generally best to open up the field by about one standard deviation of the positional uncertainty--that is sigma/2 on each side of the target volume. In principle it is simultaneously desirable to increase the prescribed dose by a few per cent compared to the case where the positional uncertainty is negligible, in order to compensate for the rounded shoulders of the delivered dose distribution. When the tissues surrounding the tumor no longer are dose limiting even larger increases in field size may be advantageous. For more critical clinical situations the positional uncertainty may even limit the success of radiotherapy. In such cases one generally wants to create a steeper dose distribution than the underlying random Gaussian displacement process allows. The problem is then best handled by quantifying the treatment outcome under the influence of the stochastic process of patient misalignment. Either the coincidence with the desired dose distribution, or the expectation value of the probability of achieving complication-free tumor control is maximized under the influence of this stochastic process. It is shown that the most advantageous treatment is to apply beams that are either considerably widened or slightly widened and over flattened near the field edges for small and large fraction numbers respectively.

Dose-Response Relationship, Radiation↗

Weak convergence of a sequence of stochastic difference equations to a stochastic ordinary differential equation.

We consider a sequence of discrete parameter stochastic processes defined by solutions to stochastic difference equations. A condition is given that this sequence converges weakly to a continuous parameter process defined by solutions to a stochastic ordinary differential equation. Applying this result, two limit theorems related to population biology are proved. Random parameters in stochastic difference equations are autocorrelated stationary Gaussian processes in the first case. They are jump-type Markov processes in the second case. We discuss a problem of continuous time approximations for discrete time models in random environments.

Genetics, Population↗

Theoretical estimates for the largest Lyapunov exponent of many-particle systems.

The largest Lyapunov exponent of an ergodic Hamiltonian system is the rate of exponential growth of the norm of a typical vector in the tangent space. For an N-particle Hamiltonian system with a smooth Hamiltonian of the type p(2)+V(q), the evolution of tangent vectors is governed by the Hessian matrix V of the potential. Ergodicity implies that the Lyapunov exponent is independent of initial conditions on the energy shell, which can then be chosen randomly according to the microcanonical distribution. In this way, a stochastic process V(t) is defined, and the evolution equation for tangent vectors can now be seen as a stochastic differential equation. An equation for the evolution of the average squared norm of a tangent vector can be obtained using the standard theory in which the average propagator is written as a cumulant expansion. We show that if cumulants higher than the second one are discarded, the Lyapunov exponent can be obtained by diagonalizing a small-dimension matrix that in some cases can be as small as 3 x 3. In all cases, the matrix elements of the propagator are expressed in terms of correlation functions of the stochastic process. We discuss the connection between our approach and an alternative theory, the so-called geometric method.

Journal Article↗

Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains.

The statistical analysis of two simultaneously observed trains of neuronal spikes is described, using as a conceptual framework the theory of stochastic point processes.The first statistical question that arises is whether the observed trains are independent; statistical techniques for testing independence are developed around the notion that, under the null hypothesis, the times of spike occurrence in one train represent random instants in time with respect to the other. If the null hypothesis is rejected-if dependence is attributed to the trains-the problem then becomes that of characterizing the nature and source of the observed dependencies. Statistical signs of various classes of dependencies, including direct interaction and shared input, are discussed and illustrated through computer simulations of interacting neurons. The effects of nonstationarities on the statistical measures for simultaneous spike trains are also discussed. For two-train comparisons of irregularly discharging nerve cells, moderate nonstationarities are shown to have little effect on the detection of interactions.Combining repetitive stimulation and simultaneous recording of spike trains from two (or more) neurons yields additional clues as to possible modes of interaction among the monitored neurons; the theory presented is illustrated by an application to experimentally obtained data from auditory neurons.A companion paper covers the analysis of single spike trains.

Action Potentials↗

Functional integration approach to hysteresis.

A general formulation of scalar hysteresis is proposed. This formulation is based on two steps. First, a generating function g(x) is associated with an individual system, and a hysteresis evolution operator is defined by an appropriate envelope construction applied to g(x), inspired by the overdamped dynamics of systems evolving in multistable free-energy landscapes. Second, the average hysteresis response of an ensemble of such systems is expressed as a functional integral over the space G of all admissible generating functions, under the assumption that an appropriate measure mu has been introduced in G. The consequences of the formulation are analyzed in detail in the case where the measure mu is generated by a continuous, Markovian stochastic process. The calculation of the hysteresis properties of the ensemble is reduced to the solution of the level-crossing problem for the stochastic process. In particular, it is shown that, when the process is translationally invariant (homogeneous), the ensuing hysteresis properties can be exactly described by the Preisach model of hysteresis, and the associated Preisach distribution is expressed in closed analytic form in terms of the drift and diffusion parameters of the Markovian process. Possible applications of the formulation are suggested, concerning the interpretation of magnetic hysteresis due to domain wall motion in quenched-in disorder and the interpretation of critical state models of superconducting hysteresis.

Journal Article↗

A microeconometric analysis of Canadian health care utilization.

Understanding health care utilization is important to design efficient and effective health systems. Toward this end, we develop a relatively simple and intuitively appealing microeconometric framework to analyse health care utilization and illustrate its use with recent Canadian microdata. We find that health care utilization consists of distinct stochastic processes requiring the use of two stochastic regression models. In particular, the latent class modelling framework is the superior statistical framework if the data permit modelling unobserved heterogeneity and overdispersion. In many instances, results differ for the classes of high and low users of health care services.

Adolescent↗

Damped stochastic system driven by colored noise: analytical solution by a path integral approach

We consider the nonlinear non-Markovian stochastic process associated with the damped nonlinear dynamical system driven by Ornstein-Uhlenbeck noise. An approximate Fokker-Planck-type equation governing the above stochastic process is derived using the path-integral approach. The stationary probability density function (SPDF) of the above process is then computed using the matrix continued fraction method. The SPDF compares favorably with the corresponding digital simulation results obtained by us.

Journal Article↗

Ancestral processes in population genetics-the coalescent.

A special stochastic process, called the coalescent, is of fundamental interest in population genetics. For a large class of population models this process is the appropriate tool to analyse the ancestral structure of a sample of n individuals or genes, if the total number of individuals in the population is sufficiently large. A corresponding convergence theorem was first proved by Kingman in 1982 for the Wright-Fisher model and the Moran model. Generalizations to a large class of exchangeable population models and to models with overlying mutation processes followed shortly later. One speaks of the "robustness of the coalescent, as this process appears in many models as the total population size tends to infinity. This publication can be considered as an introduction to the theory of the coalescent as well as a review of the most important "convergence-to-the-coalescent-theorems. Convergence theorems are not only presented for the classical exchangeable haploid case but also for larger classes of population models, for example for diploid, two-sex or non-exchangeable models. A review-like summary of further examples and applications of convergence to the coalescent is given including the most important biological forces like mutation, recombination and selection. The general coalescent process allows for simultaneous multiple mergers of ancestral lines.

Animals↗

Mathematical models of eye movements in reading: a possible role for autonomous saccades.

An efficient method for the exact numerical simulation of semi-Markov processes is used to study minimal models of the control of eye movements in reading. When we read a text, typical sequences of fixations form a rather complicated trajectory - almost like a random walk. Mathematical models of eye movement control can account for this behavior using stochastic transition rules between few discrete internal states, which represent combinations of certain stages of lexical access and saccade programs. We show that experimentally observed fixation durations can be explained by residence-time-dependent transition probabilities. Stochastic processes with this property are known as semi-Markov processes. For our numerical simulations we use the minimal process method (Gillespie algorithm), which is an exact and efficient simulation algorithm for this class of stochastic processes. Within this mathematical framework, we study different forms of coupling between eye movements and shifts of covert attention in reading. Our model lends support to the existence of autonomous saccades, i.e., the hypothesis that initiations of saccades are not completely determined by lexical access processes.

Algorithms↗

Random variation and concentration effects in PCR.

Even though the efficiency of the polymerase chain reaction (PCR) reaction decreases, analyses are made in terms of Galton-Watson processes, or simple deterministic models with constant replication probability (efficiency). Recently, Schnell and Mendoza have suggested that the form of the efficiency, can be derived from enzyme kinetics. This results in the sequence of molecules numbers forming a stochastic process with the properties of a branching process with population size dependence, which is supercritical, but has a mean reproduction number that approaches one. Such processes display ultimate linear growth, after an initial exponential phase, as is the case in PCR. It is also shown that the resulting stochastic process for a large Michaelis-Menten constant behaves like the deterministic sequence x(n) arising by iterations of the function f(x)=x+x/(1+x).

Genetic Variation↗

Two mechanisms underlie processing of stochastic motion stimuli.

We have constructed "limited lifetime" stochastic motion stimuli using Gabor functions instead of dots, thereby controlling the local attributes of spatial frequency and orientation. Human psychophysical data for direction discrimination using these stimuli reveal two qualitatively distinct kinds of processing. For small displacements, direction discrimination performance as a function of displacement is scaled with spatial frequency in a manner consistent with a linear filtering motion mechanism. Motion perception for relatively large displacements is not directly related to the spatial frequency, and is consistent with a nonlinear process which signals motion of contrast envelopes.

Discrimination, Psychological↗

Two-state stochastic models for memory in ion channels.

AIM: To study quantitatively the memory existing in ion channels. METHODS: Stochastic processes were used to model 2 categories of memory (short-term and long-term) by persisting in the standpoint of two-state, instead of multiple states, but with different transition mechanism. RESULTS: A two-state Markov process with constant transition intensities well fitted the short-term memory and a two-state Markov process within a kind of random environment well fitted the long-term memory. Statistical procedures for parameter estimation were proposed and demonstrated with 2 real examples on the channels of PC12 cells. CONCLUSION: The memory in ion channels can be quantitatively modelled as stochastic process with 2 states.

Adrenal Gland Neoplasms↗

Statistical properties of color-signal spaces.

In applications of principal component analysis (PCA) it has often been observed that the eigenvector with the largest eigenvalue has only nonnegative entries when the vectors of the underlying stochastic process have only nonnegative values. This has been used to show that the coordinate vectors in PCA are all located in a cone. We prove that the nonnegativity of the first eigenvector follows from the Perron-Frobenius (and Krein-Rutman theory). Experiments show also that for stochastic processes with nonnegative signals the mean vector is often very similar to the first eigenvector. This is not true in general, but we first give a heuristical explanation why we can expect such a similarity. We then derive a connection between the dominance of the first eigenvalue and the similarity between the mean and the first eigenvector and show how to check the relative size of the first eigenvalue without actually computing it. In the last part of the paper we discuss the implication of theoretical results for multispectral color processing.

Algorithms↗

Accelerated degradation models for failure based on geometric Brownian motion and gamma processes.

Based on a generalized cumulative damage approach with a stochastic process describing degradation, new accelerated life test models are presented in which both observed failures and degradation measures can be considered for parametric inference of system lifetime. Incorporating an accelerated test variable, we provide several new accelerated degradation models for failure based on the geometric Brownian motion or gamma process. It is shown that in most cases, our models for failure can be approximated closely by accelerated test versions of Birnbaum-Saunders and inverse Gaussian distributions. Estimation of model parameters and a model selection procedure are discussed, and two illustrative examples using real data for carbon-film resistors and fatigue crack size are presented.

Equipment Failure Analysis↗

Scaling in critical random Boolean networks.

We derive mostly analytically the scaling behavior of the number of nonfrozen and relevant nodes in critical Kauffman networks (with two inputs per node) in the thermodynamic limit. By defining and analyzing a stochastic process that determines the frozen core we can prove that the mean number of nonfrozen nodes scales with the network size N as N(2/3), with only N(1/3) nonfrozen nodes having two nonfrozen inputs. We also show the probability distributions for the numbers of these nodes. Using a different stochastic process, we determine the scaling behavior of the number of relevant nodes. Their mean number increases for large N as N(1/3), and only a finite number of relevant nodes have two relevant inputs. It follows that all relevant components apart from a finite number are simple loops and that the mean number and length of attractors increases faster than any power law with network size.

Journal Article↗

[Partial lease squares approach to functional analysis].

We extend the partial least squares (PLS) approach to functional data represented in our models by sample paths of stochastic process with continuous time. Due to the infinite dimension, when functional data are used as a predictor for linear regression and classification models, the estimation problem is an ill-posed one. In this context, PLS offers a simple and efficient alternative to the methods based on the principal components of the stochastic process. We compare the results given by the PLS approach and other linear models using several datasets from economy, industry and medical fields.

Biometry↗

A new method to predict the evolution of the power spectral density for a finite-amplitude sound wave.

A method to predict the effect of nonlinearity on the power spectral density of a plane wave traveling in a thermoviscous fluid is presented. As opposed to time-domain methods, the method presented here is based directly on the power spectral density of the signal, not the signal itself. The Burgers equation is employed for the mathematical description of the combined effects of nonlinearity and dissipation. The Burgers equation is transformed into an infinite set of linear equations that describe the evolution of the joint moments of the signal. A method for solving this system of equations is presented. Only a finite number of equations is appropriately selected and solved by numerical means. For the method to be applied all appropriate joint moments must be known at the source. If the source condition has Gaussian characteristics (it is a Gaussian noise signal or a Gaussian stationary and ergodic stochastic process), then all the joint moments can be computed from the power spectral density of the signal at the source. Numerical results from the presented method are shown to be in good agreement with known analytical solutions in the preshock region for two benchmark cases: (i) sinusoidal source signal and (ii) a Gaussian stochastic process as the source condition.

Journal Article↗

Stochastic differential equations, their interpretation and application.

The work is recommended to readers with some, maybe heuristic, imagine about stochastic processes that want to meet the notion stochastic differential equation and its interpretation. The notions like Brownian motion and stochastic integral with interpretations in concrete situations in areas of biology and medicine are discussed. The questions are related to mathematical modelling and they may be interpreted in connection with stochastic signal filtering and optimal queuing theory.

Stochastic Processes↗