Leveraging whole genome sequencing data for demographic inference with approximate Bayesian computation

Accounting for historical demographic features, such as the strength and timing of gene flow and divergence times between closely related lineages, is vital for many inferences in evolutionary biology. Approximate Bayesian computation (ABC) is one method commonly used to estimate demographic parameters. However, the DNA sequences used as input for this method, often microsatellites or RADseq loci, usually represent a small fraction of the genome. Whole genome sequencing (WGS) data, on the other hand, have been used less often with ABC, and questions remain about the potential benefit of, and how to best implement, this type of data; we used pseudo‐observed data sets to explore such questions. Specifically, we addressed the potential improvements in parameter estimation accuracy that could be associated with WGS data in multiple contexts; namely, we quantified the effects of (a) more data, (b) haplotype‐based summary statistics, and (c) locus length. Compared with a hypothetical RADseq data set with 2.5 Mbp of data, using a 1 Gbp data set consisting of 100 Kbp sequences led to substantial gains in the accuracy of parameter estimates, which was mostly due to haplotype statistics and increased data. We also quantified the effects of including (a) locus‐specific recombination rates, and (b) background selection information in ABC analyses. Importantly, assuming uniform recombination or ignoring background selection had a negative effect on accuracy in many cases. Software and results from this method validation study should be useful for future demographic history analyses.     

Effects of Organic Solvents on the Coupling Between ATP Hydrolysis and Sliding of Microtubules in Axonemes from Chlamydomonas Flagella

ABSTRACT. The effects of organic solvents on the ATPase activity and the sliding disintegration of axonemes from Chlamydomonas were investigated. The axonemal ATPase was markedly activated by methanol accompanying with marked inhibition of the sliding disintegration of axonemes. On the contrary, glycerol inhibited the ATPase activity without serious inhibition of the sliding disintegration. As far as the axonemes are not irreversibly denatured by extremely high concentration of solvents, the effects of solvents both on the ATPase and the ability of sliding are reversible. Therefore, the inhibition of sliding accompanied by the activation of ATPase is probably due to an inability to couple the hydrolysis of ATP to sliding between dynein and microtubule in the presence of methanol. The axonemal ATPase was less sensitive to vanadate inhibition after exposure to methanol. This indicates that methanol makes the dyneinADP.Pi complex unstable and increases product release. On the other hand, glycerol and ethylene glycol seem to stabilize the force generation responsible for the sliding through stabilizing the dynein.ADP.Pi complex.     

The Treatment of Missing Values in Logistic Regression

The efficiencies of the estimators in the linear logistic regression model are examined using simulations under six missing value treatments. These treatments use either the maximum likelihood or the discriminant function approach in the estimation of the regression coefficients. Missing values are assumed to occur at random. The cases of multivariate normal and dichotomous independent variables are both considered. We found that in general, there is no uniformly best method. However, mean substitution and discriminant function estimation using existing pairs of values for correlations turn out to be favourable for the cases considered.     

The Monofactorial Model for Recurrence Risks with Generalized Penetrance

Recurrence risks are derived explicitly in terms of gene frequencies and penetrance coefficients for the general case in which all genotypes have incomplete penetrance. Maximum likelihood estimation of recurrence risks is achieved through the use of the semi-symmetric intraclass contingency table. The resulting formulas and estimation procedure can be useful for the analysis of population and family data, and in genetic counselling.     

Decision Support through Mass and Energy Flow Management in the Vehicle-Refinishing Sector

In the past few decades, major advances in environmental protection within the coating application industry have been made. In spite of this technological progress, approximately 50% of industrial-solvent emissions still come from the paint-application sector. The advances made in reducing emissions for plants requiring licensing have unfortunately had no influence on the environmental efforts of smaller companies. Solvent-reduced painting systems, such as high-solid paints, water-based coating, and powder coating have not been able to achieve acceptance, nor have innovative application technologies. The principal arguments against a conversion to these ecologically more favorable alternatives were related to cost and quality.
Recently, the EU Solvent Directive (1999/13/EC) went into effect, aiming to significantly reduce industrial-solvent emissions. Up until this point, however, instruments enabling smaller companies to determine their solvent emissions and to simultaneously develop process-improvement potentials while keeping costs in mind have been missing.
Using the mass and energy flow-management approach, cost structures and environmental benefits can be made transparent to the entrepreneur. The primary result of the research projects presented here is the computer-based mass and energy flow model called the individual computer-aided mass and energy flow model for the vehicle-refinishing sector (IMPROVE). It can be used as a detailed business-consultancy tool. Based upon this, practical guidelines were developed for easy orientation and activity planning. They can be used by companies to help them fulfill the requirements of environmental legislation and to display the benefits that can be achieved by various emission-reduction measures.