Identification and separation of DNA mixtures using peak area information (Updated version of Statistical Research Paper No. 25)

We introduce a new methodology, based upon probabilistic expert systems, for analysing forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions. The expert system can be used for ascertaining whether individuals, whose profiles have been measured, have contributed to the mixture, but also to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of our probabilistic methodology is illustrated on case data examples and compared with alternative approaches. The advantages are that identification and separation issues can be handled in a unified way within a single probabilistic model and the uncertainty associated with the analysis is quantified. Further work, required to bring the methodology to a point where it could be applied to the routine analysis of casework, is discussed

Probabilistic expert systems for handling artifacts in complex DNA mixtures

This paper presents a coherent probabilistic framework for taking account of allelic dropout, stutter bands and silent alleles when interpreting STR DNA profiles from a mixture sample using peak size information arising from a PCR analysis. This information can be exploited for evaluating the evidential strength for a hypothesis that DNA from a particular person is present in the mixture. It extends an earlier Bayesian network approach that ignored such artifacts. We illustrate the use of the extended network on a published casework example

Thermal feedback in Si JFETs operating at low temperatures

Thermal feedback theory for silicon junction FET operating at low temperature

Characterization of Black Walnut Genotypes for Resistance to Thousand Cankers Disease, Frost Hardiness and Other Desirable Horticultural Traits

The black walnut, Juglans nigra L., is native to the United States (USA) and is a valuable timber and nut tree. Just before the beginning of the 21st century, several western states observed a decline in the health and, later, death of black walnut trees. The pathogen-vector complex now known as thousand cankers disease (TCD) was shown to be the cause. The disease, caused by Geosmithia morbida Kolařik, is vectored by the walnut twig beetle (WTB), Pityophthorus juglandis Blackman. Thousands of WTB will swarm and enter a tree vectoring the fungus at each entry point where cankers then develop, quickly expand, coalesce and kill the branch or stem. The disease has been confirmed across the USA and in parts of Europe. The research and development of resistant cultivars is important to maintain native populations and livelihoods. The purpose of this project was to evaluate black walnut and hybrid trees for resistance to TCD through direct inoculation with the pathogen G. morbida. Inoculation of limbs took place in early summer of 2015, 2016 and 2017 at the Cyril Reed Funk Research Farm in Richmond, UT and Dayton, ID. Inoculated limbs were removed from the tree after senescence and canker size measured. An average of 336 trees were inoculated. One tree consistently exhibited resistance to TCD indicated by no canker staining. An additional 14 trees exhibited resistance for two of the three years. The results of this project indicate that breeding for resistance to TCD could be a management option for the disease

Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property

The AMP Markov property is a recently proposed alternative Markov property for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced LWF Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm.Comment: 15 pages, article will appear in Scandinavian Journal of Statistic

Transfer Entropy as a Log-likelihood Ratio

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic chi-squared distribution is established for the transfer entropy estimator. The result generalises the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense

Hierarchical Models for Independence Structures of Networks

We introduce a new family of network models, called hierarchical network models, that allow us to represent in an explicit manner the stochastic dependence among the dyads (random ties) of the network. In particular, each member of this family can be associated with a graphical model defining conditional independence clauses among the dyads of the network, called the dependency graph. Every network model with dyadic independence assumption can be generalized to construct members of this new family. Using this new framework, we generalize the Erd\"os-R\'enyi and beta-models to create hierarchical Erd\"os-R\'enyi and beta-models. We describe various methods for parameter estimation as well as simulation studies for models with sparse dependency graphs.Comment: 19 pages, 7 figure

The physical determinants of the thickness of lamellar polymer crystals

Based upon kinetic Monte Carlo simulations of crystallization in a simple polymer model we present a new picture of the mechanism by which the thickness of lamellar polymer crystals is constrained to a value close to the minimum thermodynamically stable thickness. This description contrasts with those given by the two dominant theoretical approaches.Comment: 4 pages, 4 figures, revte