Heart rate changes during partial seizures: A study amongst Singaporean patients

INTRODUCTION: Studies in Europe and America showed that tachycardia, less often bradycardia, frequently accompanied partial seizures in Caucasian patients. We determine frequency, magnitude and type of ictal heart rate changes during partial seizures in non-Caucasian patients in Singapore. METHODS: Partial seizures recorded during routine EEGs performed in a tertiary hospital between 1995 and 1999 were retrospectively reviewed. All routine EEGs had simultaneous ECG recording. Heart rate before and during seizures was determined and correlated with epileptogenic focus. Differences in heart rate before and during seizures were grouped into 4 types: (1) >10% decrease; (2) -10 to +20% change; (3) 20–50% increase; (3) >50% increase. RESULTS: Of the total of 37 partial seizures, 18 were left hemisphere (LH), 13 were right hemisphere (RH) and 6 were bilateral (BL) in onset. 51% of all seizures showed no significant change in heart rate (type 2), 22% had moderate sinus tachycardia (type 3), 11% showed severe sinus tachycardia (type 4), while 16% had sinus bradycardia (type 1). Asystole was recorded in one seizure. Apart from having more tachycardia in bilateral onset seizures, there was no correlation between side of ictal discharge and heart rate response. Compared to Caucasian patients, sinus tachycardia was considerably less frequent. Frequency of bradycardia was similar to those recorded in the literature. CONCLUSIONS: Significant heart rate changes during partial seizures were seen in half of Singaporean patients. Although sinus tachycardia was the most common heart rate change, the frequency was considerably lower compared to Caucasian patients. This might be due to methodological and ethnic differences. Rates of bradycardia are similar to those recorded in the literature

An Evolutionary Reduction Principle for Mutation Rates at Multiple Loci

A model of mutation rate evolution for multiple loci under arbitrary selection is analyzed. Results are obtained using techniques from Karlin (1982) that overcome the weak selection constraints needed for tractability in prior studies of multilocus event models. A multivariate form of the reduction principle is found: reduction results at individual loci combine topologically to produce a surface of mutation rate alterations that are neutral for a new modifier allele. New mutation rates survive if and only if they fall below this surface - a generalization of the hyperplane found by Zhivotovsky et al. (1994) for a multilocus recombination modifier. Increases in mutation rates at some loci may evolve if compensated for by decreases at other loci. The strength of selection on the modifier scales in proportion to the number of germline cell divisions, and increases with the number of loci affected. Loci that do not make a difference to marginal fitnesses at equilibrium are not subject to the reduction principle, and under fine tuning of mutation rates would be expected to have higher mutation rates than loci in mutation-selection balance. Other results include the nonexistence of 'viability analogous, Hardy-Weinberg' modifier polymorphisms under multiplicative mutation, and the sufficiency of average transmission rates to encapsulate the effect of modifier polymorphisms on the transmission of loci under selection. A conjecture is offered regarding situations, like recombination in the presence of mutation, that exhibit departures from the reduction principle. Constraints for tractability are: tight linkage of all loci, initial fixation at the modifier locus, and mutation distributions comprising transition probabilities of reversible Markov chains.Comment: v3: Final corrections. v2: Revised title, reworked and expanded introductory and discussion sections, added corollaries, new results on modifier polymorphisms, minor corrections. 49 pages, 64 reference

Boundary-layer turbulence as a kangaroo process

A nonlocal mixing-length theory of turbulence transport by finite size eddies is developed by means of a novel evaluation of the Reynolds stress. The analysis involves the contruct of a sample path space and a stochastic closure hypothesis. The simplifying property of exhange (strong eddies) is satisfied by an analytical sampling rate model. A nonlinear scaling relation maps the path space onto the semi-infinite boundary layer. The underlying near-wall behavior of fluctuating velocities perfectly agrees with recent direct numerical simulations. The resulting integro-differential equation for the mixing of scalar densities represents fully developed boundary-layer turbulence as a nondiffusive (Kubo-Anderson or kangaroo) type of stochastic process. The model involves a scaling exponent (with → in the diffusion limit). For the (partly analytical) solution for the mean velocity profile, excellent agreement with the experimental data yields 0.58. © 1995 The American Physical Society

Learning Poisson Binomial Distributions

We consider a basic problem in unsupervised learning: learning an unknown \emph{Poisson Binomial Distribution}. A Poisson Binomial Distribution (PBD) over $\{0,1,\dots,n\}$ is the distribution of a sum of $n$ independent Bernoulli random variables which may have arbitrary, potentially non-equal, expectations. These distributions were first studied by S. Poisson in 1837 \cite{Poisson:37} and are a natural $n$-parameter generalization of the familiar Binomial Distribution. Surprisingly, prior to our work this basic learning problem was poorly understood, and known results for it were far from optimal. We essentially settle the complexity of the learning problem for this basic class of distributions. As our first main result we give a highly efficient algorithm which learns to \eps-accuracy (with respect to the total variation distance) using \tilde{O}(1/\eps^3) samples \emph{independent of $n$}. The running time of the algorithm is \emph{quasilinear} in the size of its input data, i.e., \tilde{O}(\log(n)/\eps^3) bit-operations. (Observe that each draw from the distribution is a $\log(n)$-bit string.) Our second main result is a {\em proper} learning algorithm that learns to \eps-accuracy using \tilde{O}(1/\eps^2) samples, and runs in time (1/\eps)^{\poly (\log (1/\eps))} \cdot \log n. This is nearly optimal, since any algorithm {for this problem} must use \Omega(1/\eps^2) samples. We also give positive and negative results for some extensions of this learning problem to weighted sums of independent Bernoulli random variables.Comment: Revised full version. Improved sample complexity bound of O~(1/eps^2