Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power

Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F-0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F-0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (D-LR) appeared to be an effective way to predict whether F-0 immigrants could be identified for a particular pair of populations using a given set of markers.

Critical relationship between sodium valproate dose and human teratogenicity: results of the Australian register of anti-epileptic drugs in pregnancy

To compare the incidence of foetal malformations (FMs) in pregnant women with epilepsy treated with different anti-epileptic drugs (AED) and doses, and the influence of seizures, family and personal history, and environmental factors. A prospective, observational, community-based cohort study. Methods. A voluntary, Australia-wide, telephone-interview-based register prospectively enrolling three groups of pregnant women: taking AEDs for epilepsy; with epilepsy not taking AEDs; taking AEDs for a non-epileptic indication. Four hundred and fifty eligible women were enrolled over 40 months. Three hundred and ninety six pregnancies had been completed, with 7 sets of twins, for a total of 403 pregnancy outcomes. Results. 354 (87.8%) pregnancy outcomes resulted in a healthy live birth, 26 (6.5%) had a FM, 4 (1%) a death in utero, 1 (0.2%) a premature labour with stillbirth, 14 (3.5%) a spontaneous abortion and 4 lost to follow-up. The FM rate was greater in pregnancies exposed to sodium valproate (VPA) in the first trimester (116.0%) compared with those exposed to all other AEDs (16.0% vs. 2.4%, P < 0.01) or no AEDs (16.0% vs. 3.1 %, P < 0.01). The mean daily dose of VPA taken in pregnancy with FMs was significantly greater than in those without (11975 vs: 1128 mg, P < 0.01). The incidence of FM with VPA doses greater than or equal to 1100 mg was 30.2% vs. 3.2% with doses < 1100 mg (P < 0.01). Conclusions. There is a dose-effect relationship for FM and exposure to VPA during the first trimester of pregnancy, with higher doses of VPA associated with a significantly greater risk than with lower doses or with other AEDs. These results highlight the need to limit, where possible, the dose of VPA in pregnancy. (C) 2004 Elsevier Ltd. All rights reserved.

Numerical methods for strong solutions of stochastic differential equations: an overview

This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations. We give a brief survey of the area focusing on a number of application areas where approximations to strong solutions are important, with a particular focus on computational biology applications, and give the necessary analytical tools for understanding some of the important concepts associated with stochastic processes. We present the stochastic Taylor series expansion as the fundamental mechanism for constructing effective numerical methods, give general results that relate local and global order of convergence and mention the Magnus expansion as a mechanism for designing methods that preserve the underlying structure of the problem. We also present various classes of explicit and implicit methods for strong solutions, based on the underlying structure of the problem. Finally, we discuss implementation issues relating to maintaining the Brownian path, efficient simulation of stochastic integrals and variable-step-size implementations based on various types of control.

Chronic hepatitis B: A critical appraisal of current approaches to therapy

Background & Aims: Treatment of chronic hepatitis B (CHB) involves a number of complex and controversial issues. Expert opinions may differ from those of practicing hepatologists and gastroenterologists. We aimed to explore this issue further after a critical review of the literature. Methods: A panel of 14 international experts graded the strength of evidence for 16 statements addressing 3 content areas: patient selection, therapeutic end points, and treatment options. Available data relating to the statements were reviewed critically in 3 small work groups. After discussion of each statement with the entire panel, the experts voted anonymously to accept or reject statements based on the strength of evidence and their experience. A total of 241 members of the American Association for the Study of Liver Diseases (AASLD) responded to the same statements and their responses were compared with those of the experts. A discordant response was defined as a difference of more than 20% in any of the 5 graded levels of response (accept or reject) between the 2 groups. Results: With the exception of 2 statements, the experts’ responses were relatively uniform. However, the responses of the AASLD members were discordant from the experts in 12 statements, spanning all 3 content areas. Conclusions: Several areas of disagreement on the management of CHB exist between experts and AASLD members. Our results indicate a potential knowledge gap among practicing hepatologists. Better educational efforts are needed to meet the challenge of managing this complex disorder in which even expert opinion occasionally may disagree.

Expansion-based algorithms for finding single pair shortest path on surface

Finding single pair shortest paths on surface is a fundamental problem in various domains, like Geographic Information Systems (GIS) 3D applications, robotic path planning system, and surface nearest neighbor query in spatial database, etc. Currently, to solve the problem, existing algorithms must traverse the entire polyhedral surface. With the rapid advance in areas like Global Positioning System (CPS), Computer Aided Design (CAD) systems and laser range scanner, surface models axe becoming more and more complex. It is not uncommon that a surface model contains millions of polygons. The single pair shortest path problem is getting harder and harder to solve. Based on the observation that the single pair shortest path is in the locality, we propose in this paper efficient methods by excluding part of the surface model without considering them in the search process. Three novel expansion-based algorithms are proposed, namely, Naive algorithm, Rectangle-based Algorithm and Ellipse-based Algorithm. Each algorithm uses a two-step approach to find the shortest path. (1) compute an initial local path. (2) use the value of this initial path to select a search region, in which the global shortest path exists. The search process terminates once the global optimum criteria are satisfied. By reducing the searching region, the performance is improved dramatically in most cases.

Accelerated leap methods for simulating discrete stochastic chemical kinetics

Biologists are increasingly conscious of the critical role that noise plays in cellular functions such as genetic regulation, often in connection with fluctuations in small numbers of key regulatory molecules. This has inspired the development of models that capture this fundamentally discrete and stochastic nature of cellular biology - most notably the Gillespie stochastic simulation algorithm (SSA). The SSA simulates a temporally homogeneous, discrete-state, continuous-time Markov process, and of course the corresponding probabilities and numbers of each molecular species must all remain positive. While accurately serving this purpose, the SSA can be computationally inefficient due to very small time stepping so faster approximations such as the Poisson and Binomial τ-leap methods have been suggested. This work places these leap methods in the context of numerical methods for the solution of stochastic differential equations (SDEs) driven by Poisson noise. This allows analogues of Euler-Maruyuma, Milstein and even higher order methods to be developed through the Itô-Taylor expansions as well as similar derivative-free Runge-Kutta approaches. Numerical results demonstrate that these novel methods compare favourably with existing techniques for simulating biochemical reactions by more accurately capturing crucial properties such as the mean and variance than existing methods.