A new approach for estimating the efficiencies of the nucleotide substitution models

In this article, a new approach is presented for estimating the efficiencies of the nucleotide substitution models in a four-taxon case and then this approach is used to estimate the relative efficiencies of six substitution models under a wide variety of conditions. In this approach, efficiencies of the models are estimated by using a simple probability distribution theory. To assess the accuracy of the new approach, efficiencies of the models are also estimated by using the direct estimation method. Simulation results from the direct estimation method confirmed that the new approach is highly accurate. The success of the new approach opens a unique opportunity to develop analytical methods for estimating the relative efficiencies of the substitution models in a straightforward way.     

Donor Conception Disclosure: Directive or Non-Directive Counselling?

It is widely agreed among health professionals that couples using donor insemination should be offered counselling on the topic of donor conception disclosure. However, it is clear from the literature that there has long been a lack of agreement about which counselling approach should be used in this case: a directive or a non-directive approach. In this paper we investigate which approach is ethically justifiable by balancing the two underlying principles of autonomy (non-directive approach) and beneficence (directive approach). To overrule one principle in favour of another, six conditions should be fulfilled. We analyse the arguments in favour of the beneficence principle, and consequently, a directive approach. This analysis shows that two conditions are not met; the principle of autonomy should not be overridden. Therefore, at this moment, a directive counselling approach on donor conception disclosure cannot be ethically justified.     

A mixed dispatching rule approach in FMS scheduling

Short-term scheduling in flexible manufacturing systems (FMSs) is a difficult problem because of the complexities and dynamic behavior of FMSs. To solve this problem, a dispatching rule approach is widely used. In this approach, however, a single dispatching rule is usually assigned for all machines in a system during a given scheduling interval. In this paper, a mixed dispatching rule which can assign a different dispatching rule for each machine is proposed. A search algorithm which selects an appropriate mixed dispatching rule using predictions based on discrete event simulation is developed for this approach. The search algorithm for the mixed dispatching rule is described in detail. The effectiveness (in meeting performance criteria) of the mixed dispatching rule and the efficiency of the search algorithm relative to exhaustive search (complete enumeration) is demonstrated on an FMS model. The mixed dispatching rule approach performs up to 15.9% better than the conventional approach, and is 4% better on average. The statistical significance of the results is dicussed.     

Phylogeny-guided (meta)genome mining approach for the targeted discovery of new microbial natural products

Genomics-based methods are now commonplace in natural products research. A phylogeny-guided mining approach provides a means to quickly screen a large number of microbial genomes or metagenomes in search of new biosynthetic gene clusters of interest. In this approach, biosynthetic genes serve as molecular markers, and phylogenetic trees built with known and unknown marker gene sequences are used to quickly prioritize biosynthetic gene clusters for their metabolites characterization. An increase in the use of this approach has been observed for the last couple of years along with the emergence of low cost sequencing technologies. The aim of this review is to discuss the basic concept of a phylogeny-guided mining approach, and also to provide examples in which this approach was successfully applied to discover new natural products from microbial genomes and metagenomes. I believe that the phylogeny-guided mining approach will continue to play an important role in genomics-based natural products research.     

Sequence-based prioritization of nonsynonymous single-nucleotide polymorphisms for the study of disease mutations

The increasing demand for the identification of genetic variation responsible for common diseases has translated into a need for sophisticated methods for effectively prioritizing mutations occurring in disease-associated genetic regions. In this article, we prioritize candidate nonsynonymous single-nucleotide polymorphisms (nsSNPs) through a bioinformatics approach that takes advantages of a set of improved numeric features derived from protein-sequence information and a new statistical learning model called "multiple selection rule voting" (MSRV). The sequence-based features can maximize the scope of applications of our approach, and the MSRV model can capture subtle characteristics of individual mutations. Systematic validation of the approach demonstrates that this approach is capable of prioritizing causal mutations for both simple monogenic diseases and complex polygenic diseases. Further studies of familial Alzheimer diseases and diabetes show that the approach can enrich mutations underlying these polygenic diseases among the top of candidate mutations. Application of this approach to unclassified mutations suggests that there are 10 suspicious mutations likely to cause diseases, and there is strong support for this in the literature.     

SVM-based learning control of space robots in capturing operation

In this paper, we presents a novel approach for tracking and catching operation of space robots using learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parametrize the model of HCS. Then we develop a new SVM-based learning structure to better implement human control strategy learning in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, and so on. Therefore this approach is efficient in modeling, understanding and transferring its learning process. The simulation results attest that this approach is useful and feasible in generating tracking trajectory and catching objects autonomously.     

A Bayesian model comparison approach to inferring positive selection

A popular approach to detecting positive selection is to estimate the parameters of a probabilistic model of codon evolution and perform inference based on its maximum likelihood parameter values. This approach has been evaluated intensively in a number of simulation studies and found to be robust when the available data set is large. However, uncertainties in the estimated parameter values can lead to errors in the inference, especially when the data set is small or there is insufficient divergence between the sequences. We introduce a Bayesian model comparison approach to infer whether the sequence as a whole contains sites at which the rate of nonsynonymous substitution is greater than the rate of synonymous substitution. We incorporated this probabilistic model comparison into a Bayesian approach to site-specific inference of positive selection. Using simulated sequences, we compared this approach to the commonly used empirical Bayes approach and investigated the effect of tree length on the performance of both methods. We found that the Bayesian approach outperforms the empirical Bayes method when the amount of sequence divergence is small and is less prone to false-positive inference when the sequences are saturated, while the results are indistinguishable for intermediate levels of sequence divergence.     

A constrained-likelihood approach to marker-trait association studies

Marker-trait association analysis is an important statistical tool for detecting DNA variants responsible for genetic traits. In such analyses, an analysis model of the mean genetic effects of the genotypes is often specified. For instance, the effect of the disease allele on the trait is often specified to be dominant, recessive, additive, or multiplicative. Although this model-based approach is powerful when the analysis model is correctly specified, it has been found to have low power sometimes when the specified model is incorrect. We introduce an approach that does not require the specification of a particular genetic model. This approach is built upon a constrained maximum likelihood in which the mean genetic effect of the heterozygous genotype is required to not exceed those of the two homozygous genotypes. The asymptotic distribution of the likelihood-ratio statistic is derived for two special cases. A simulation study suggests that this new approach has power comparable to that of the model-based method when the analysis model is correctly specified. This approach uses one marker at a time (i.e., it is a single-marker analysis). However, given the latest findings that powerful inferential procedures for haplotype analyses can be constructed from single-marker analyses, we expect this approach to be useful for haplotype analyses.     

The case for the hybrid capital approach for the measurement of the welfare and sustainability

This paper provides an overview of the plethora of approaches that are available to measure welfare and sustainable development. Many methods exist but there is no consensus on the ‘correct’ approach. Furthermore, we also show that the wide variety of sustainable development indictor (SDI) sets which have been adopted also show significant differences. We argue that this is mostly because many of these studies do not use a theoretical approach. We argue that the ‘capital approach’, which is used in the sustainability debate, is the most promising method to enhance international harmonization. Support is mounting in the scientific, policy and statistical communities for this approach in which economic capital, human capital, natural capital and social capital are distinguished. Many applications of this method express these capital stocks in monetary units (the ‘monetary capital approach’). This paper argues that the ‘hybrid capital approach’, in which the capital stocks can also be measured in non-monetary units, is probably more likely to achieve consensus over a large number of countries and institutes. Also a number of challenges remain for the capital approach. We argue that ideally the indicators should be based on satellite accounts of the national accounting framework. Also the capital approach could be further expanded to current welfare, progress of societies, inequality, and the international dimension of sustainability. We conclude that if the hybrid capital approach is adopted it may become easier to make consistent, theoretically sound and policy relevant comparisons between countries.     

Quantitative determination of lentiviral vector particle numbers by real-time PCR

Here, we describe a quantitative, DNA-based, real-time PCR approach to determine the number of lentivirus particles that are present in vector preparations. In this approach, the minus strong-stop cDNA fragment that is present in viral capsids serves as template for PCR. Using this technology, we found that only 0.1%-1% of the virus particles that are present in vector preparations are infectious. The approach described here is rapid, reliable, and simple in concept and can be used to estimate both vector particles in supernatants and the number of infectious particles. Also, this approach can easily be adapted to a high-throughput system by using 96-well plates and a 2-h running time.     

Calculation of absolute protein-ligand binding affinity using path and endpoint approaches

A comparative analysis is provided of rigorous and approximate methods for calculating absolute binding affinities of two protein-ligand complexes: the FKBP protein bound with small molecules 4-hydroxy-2-butanone and FK506. Our rigorous approach is an umbrella sampling technique where a potential of mean force is determined by pulling the ligand out of the protein active site over several simulation windows. The results of this approach agree well with experimentally observed binding affinities. Also assessed is a commonly used approximate endpoint approach, which separately estimates enthalpy, solvation free energy, and entropy. We show that this endpoint approach has numerous variations, all of which are prone to critical shortcomings. For example, conventional harmonic and quasiharmonic entropy estimation procedures produce disparate results for the relatively simple protein-ligand systems studied in this work.     

Petri net modeling of high-order genetic systems using grammatical evolution

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.     

A new approach for mapping quantitative trait loci using complete genetic marker linkage maps

A new approach based on nonlinear regression for the mapping of quantitative trait loci (QTLs) using complete genetic marker linkage maps is advanced in this paper. We call the approach joint mapping as it makes comprehensive use of the information from every marker locus on a chromosome. With this approach, both the detection of the existence of QTLs and the estimation of their positions, with corresponding confidence intervals, and effects can be realized simultaneously. This approach is widely applicable because only moments are used. It is simple and can save considerable computer time. It is especially useful when there are multiple QTLs and/or interactions between them on a chromosome.     

LOD Score Exclusion Analyses for Candidate QTLs using Random Population Samples

While extensive analyses have been conducted to test for, no formal analyses have been conducted to test against, the importance of candidate genes as putative QTLs using random population samples. Previously, we developed an LOD score exclusion mapping approach for candidate genes for complex diseases. Here, we extend this LOD score approach for exclusion analyses of candidate genes for quantitative traits. Under this approach, specific genetic effects (as reflected by heritability) and inheritance models at candidate QTLs can be analyzed and if an LOD score is < or = -2.0, the locus can be excluded from having a heritability larger than that specified. Simulations show that this approach has high power to exclude a candidate gene from having moderate genetic effects if it is not a QTL and is robust to population admixture. Our exclusion analysis complements association analysis for candidate genes as putative QTLs in random population samples. The approach is applied to test the importance of Vitamin D receptor (VDR) gene as a potential QTL underlying the variation of bone mass, an important determinant of osteoporosis.     

Can one angle be simply subtracted from another to determine range of motion in three-dimensional motion analysis?

To determine the range of motion of a joint between an initial orientation and a final orientation, it is convenient to subtract initial joint angles from final joint angles, a method referred to as the vectorial approach. However, for three-dimensional movements, the vectorial approach is not mathematically correct. To determine the joint range of motion, the rotation matrix between the two orientations should be calculated, and angles describing the range of motion should be extracted from this matrix, a method referred to as the matrical approach. As the matrical approach is less straightforward to implement, it is of interest to identify situations in which the vectorial approach leads to insubstantial errors. In this study, the vectorial approach was compared to the matrical approach, and theoretical justification was given for situations in which the vectorial approach can reasonably be used. The main findings are that the vectorial approach can be used if (1) the motion is planar (Woltring HJ. 1994. 3-D attitude representation of human joints: a standardization proposal. J Biomech 27(12): 1399–1414), (2) the angles between the final and the initial orientation are small (Woltring HJ. 1991. Representation and calculation of 3-D joint movement. Hum Mov Sci 10(5): 603–616), (3) the angles between the initial orientation of the distal segment and the proximal segment are small and finally (4) when only one large angle occurs between the initial orientation of the distal segment and the proximal segment and the angle sequence is chosen in such a way that this large angle occurs on the first axis of rotation. These findings provide specific criteria to consider when choosing the angle sequence to use for movement analysis.     

Is heterogeneity of catchability in capture–recapture studies a mere sampling artifact or a biologically relevant feature of the population?

The heterogeneity of catchability (HC) among the individuals encountered during a capture–recapture study has long been regarded as a liability. However, heterogeneous capture probabilities may reflect interesting but hidden features of the population, such as social status. The difficulty is to distinguish between this intrinsic heterogeneity and the extrinsic heterogeneity induced by the study itself. So far, population ecologists have not been able to distinguish between these two sources of variation in capture heterogeneity because, in the presence of heterogeneity of capture in the data, they have frequently used a too simple approach. This traditional approach, which consists of incorporating two common sources of lack of fit (transience and trap-dependence), does not directly model the HC and thus cannot investigate its biological meaning. In this context, we propose, for open populations, to directly model the HC by employing multievent models. Multievent models make it possible to break HC into two classes of catchability viewed as uncertain states. With the introduction of a coefficient of heterogeneity to model proportional probabilities of capture over time in the two classes, our approach allows the investigation of HC in a parsimonious way. In this paper, we apply both this new approach and the traditional approach to a long-term data set of male deer mice Peromyscus maniculatus. We then compare 13 candidate models separately for each approach. Our results indicate that the new approach is superior to the traditional approach. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.