Methods of weighted averaging of ECG signals using Bayesian inference and criterion function minimization

Averaging signals in time domain is one of the main methods of noise attenuation in biomedical signal processing in case of systems producing repetitive patterns such as electrocardiographic (ECG) acquisition systems. This paper presents a comprehensive study of weighted averaging of ECG signal. Presented methods use criterion function minimization, partitioning of input set of data in the time domain as well as Bayesian and empirical Bayesian framework. The existing methods are described together with their extensions. Performance of all presented methods is experimentally evaluated and compared with the traditional averaging by using arithmetic mean and well-known weighted averaging methods based on criterion function minimization (WACFM).     

Bootstrap and Asymptotic Prediction Criterion Estimates for Binomial Proportions in Insemination Data

Model choice techniques are proposed for logistic regression, based on prediction criterion estimation similar to Akaike's information criterion. For artificial insemination data of cattle, we wish to study a factor influence on success proportion; tests standard methods don't always seem suitable for prediction objective. Two prediction criterion estimate methods are applied to these data: simulated bootstrap and asymptotic estimates. Some empirical properties of this estimate are studied.     

Putting the Biological Species Concept to the Test: Using Mating Networks to Delimit Species

Although interfertility is the key criterion upon which Mayr’s biological species concept is based, it has never been applied directly to delimit species under natural conditions. Our study fills this gap. We used the interfertility criterion to delimit two closely related oak species in a forest stand by analyzing the network of natural mating events between individuals. The results reveal two groups of interfertile individuals connected by only few mating events. These two groups were largely congruent with those determined using other criteria (morphological similarity, genotypic similarity and individual relatedness). Our study, therefore, shows that the analysis of mating networks is an effective method to delimit species based on the interfertility criterion, provided that adequate network data can be assembled. Our study also shows that although species boundaries are highly congruent across methods of species delimitation, they are not exactly the same. Most of the differences stem from assignment of individuals to an intermediate category. The discrepancies between methods may reflect a biological reality. Indeed, the interfertility criterion is an environment-dependant criterion as species abundances typically affect rates of hybridization under natural conditions. Thus, the methods of species delimitation based on the interfertility criterion are expected to give results slightly different from those based on environment-independent criteria (such as the genotypic similarity criteria). However, whatever the criterion chosen, the challenge we face when delimiting species is to summarize continuous but non-uniform variations in biological diversity. The grade of membership model that we use in this study appears as an appropriate tool.     

A Missing Data Approach to Correct for Direct and Indirect Range Restrictions with a Dichotomous Criterion: A Simulation Study

A recurring methodological problem in the evaluation of the predictive validity of selection methods is that the values of the criterion variable are available for selected applicants only. This so-called range restriction problem causes biased population estimates. Correction methods for direct and indirect range restriction scenarios have widely studied for continuous criterion variables but not for dichotomous ones. The few existing approaches are inapplicable because they do not consider the unknown base rate of success. Hence, there is a lack of scientific research on suitable correction methods and the systematic analysis of their accuracies in the cases of a naturally or artificially dichotomous criterion. We aim to overcome this deficiency by viewing the range restriction problem as a missing data mechanism. We used multiple imputation by chained equations to generate complete criterion data before estimating the predictive validity and the base rate of success. Monte Carlo simulations were conducted to investigate the accuracy of the proposed correction in dependence of selection ratio, predictive validity, and base rate of success in an experimental design. In addition, we compared our proposed missing data approach with Thorndike’s well-known correction formulas that have only been used in the case of continuous criterion variables so far. The results show that the missing data approach is more accurate in estimating the predictive validity than Thorndike’s correction formulas. The accuracy of our proposed correction increases as the selection ratio and the correlation between predictor and criterion increase. Furthermore, the missing data approach provides a valid estimate of the unknown base rate of success. On the basis of our findings, we argue for the use of multiple imputation by chained equations in the evaluation of the predictive validity of selection methods when the criterion is dichotomous.     

Computational complexity of inferring phylogenies from chromosome inversion data

In systematics, parsimony methods construct phylogenies, or evolutionary trees, in which characters evolve with the least evolutionary change. The chromosome inversion, or polymorphism, parsimony criterion is used when each character of a population may exhibit homozygous or heterozygous states, but when the heterozygous state must evolve uniquely. Variations of the criterion concern whether or not the ancestral states of characters are specified. We establish that problems of inferring phylogenies by these criteria are NP-complete and thus are so difficult computationally that efficient optimal algorithms for them are unlikely to exist.     

Performance of several variable-selection methods applied to real ecological data

I evaluated the predictive ability of statistical models obtained by applying seven methods of variable selection to 12 ecological and environmental data sets. Cross-validation, involving repeated splits of each data set into training and validation subsets, was used to obtain honest estimates of predictive ability that could be fairly compared among methods. There was surprisingly little difference in predictive ability among five methods based on multiple linear regression. Stepwise methods performed similarly to exhaustive algorithms for subset selection, and the choice of criterion for comparing models (Akaike's information criterion, Schwarz's Bayesian information criterion or F statistics) had little effect on predictive ability. For most of the data sets, two methods based on regression trees yielded models with substantially lower predictive ability. I argue that there is no 'best' method of variable selection and that any of the regression-based approaches discussed here is capable of yielding useful predictive models.     

A between-class overlapping filter-based method for transcriptome data analysis

Feature selection algorithms play a crucial role in identifying and discovering important genes for cancer classification. Feature selection algorithms can be broadly categorized into two main groups: filter-based methods and wrapper-based methods. Filter-based methods have been quite popular in the literature due to their many advantages, including computational efficiency, simplistic architecture, and an intuitively simple means of discovering biological and clinical aspects. However, these methods have limitations, and the classification accuracy of the selected genes is less accurate. In this paper, we propose a set of univariate filter-based methods using a between-class overlapping criterion. The proposed techniques have been compared with many other univariate filter-based methods using an acute leukemia dataset. The following properties have been examined: classification accuracy of the selected individual genes and the gene subsets; redundancy check among selected genes using ridge regression and LASSO methods; similarity and sensitivity analyses; functional analysis; and, stability analysis. A comprehensive experiment shows promising results for our proposed techniques. The univariate filter based methods using between-class overlapping criterion are accurate and robust, have biological significance, and are computationally efficient and easy to implement. Therefore, they are well suited for biological and clinical discoveries.     

On the independent gene trees assumption in phylogenomic studies

Multilocus coalescent methods for inferring species trees or historical demographic parameters typically require the assumption that gene trees for sampled SNPs or DNA sequence loci are conditionally independent given their species tree. In practice, researchers have used different criteria to delimit “independent loci.” One criterion identifies sampled loci as being independent of each other if they undergo Mendelian independent assortment (IA criterion). O'Neill et al. (2013, Molecular Ecology, 22, 111–129) used this approach in their phylogeographic study of North American tiger salamander species complex. In two other studies, researchers developed a pair of related methods that employ an independent genealogies criterion (IG criterion), which considers the effects of population‐level recombination on correlations between the gene trees of intrachromosomal loci. Here, I explain these three methods, illustrate their use with example data, and evaluate their efficacies. I show that the IA approach is more conservative, is simpler to use and requires fewer assumptions than the IG approaches. However, IG approaches can identify much larger numbers of independent loci than the IA method, which, in turn, allows researchers to obtain more precise and accurate estimates of species trees and historical demographic parameters. A disadvantage of the IG methods is that they require an estimate of the population recombination rate. Despite their drawbacks, IA and IG approaches provide molecular ecologists with promising a priori methods for selecting SNPs or DNA sequence loci that likely meet the independence assumption in coalescent‐based phylogenomic studies.     

Goodness-of-fit in generalized nonlinear mixed-effects models

In recent years, generalized linear and nonlinear mixed-effects models have proved to be powerful tools for the analysis of unbalanced longitudinal data. To date, much of the work has focused on various methods for estimating and comparing the parameters of mixed-effects models. Very little work has been done in the area of model selection and goodness-of-fit, particularly with respect to the assumed variance-covariance structure. In this paper, we present a goodness-of-fit statistic which can be used in a manner similar to the R2 criterion in linear regression for assessing the adequacy of an assumed mean and variance-covariance structure. In addition, we introduce an approximate pseudo-likelihood ratio test for testing the adequacy of the hypothesized convariance structure. These methods are illustrated and compared to the usual normal theory likelihood methods (Akaike's information criterion and the likelihood ratio test) using three examples. Simulation results indicate the pseudo-likelihood ratio test compares favorably with the standard normal theory likelihood ratio test, but both procedures are sensitive to departures from normality.     

A comparison of different methods for estimating single-trial P300 latencies

Inferences from comparative analyses of reaction time and P300 latency are stronger when the various aspects of the distribution across trials are treated in the same way for both variables. To this end, a number of studies have resorted to estimation of P300 latency at the single-trial level. This report presents a comparative evaluation of two common methods for such single-trial analysis, i.e., peak-picking and template-matching. Both methods were applied to a representative set of real data, comprising different task conditions and two age groups. Relevant scoring parameters were varied: low-pass filter settings (down to 0.94 Hz) for peak-picking, template duration (250–970 msec) and use of covariance vs. correlation for template-matching, and use of a noise-range criterion for both methods. It is concluded that peak-picking with a 3.4 Hz filter, and template-matching using covariance and template duration between 600 and 800 msec, are best in terms of sensitivity and reliability, with peak-picking surpassing template-matching. Also, the marked increase in the number of rejected trials when the noise-range criterion was applied resulted in unwanted modulation of behavioral effects of task conditions and age groups.     

A reconciliation of local and global models for bone remodeling through optimization theory

Remodeling rules with either a global or a local mathematical form have been proposed for load-bearing bones in the literature. In the local models, the bone architecture (shape, density) is related to the strains/energies sensed at any point in the bone, while in the global models, a criterion believed to be applicable to the whole bone is used. In the present paper, a local remodeling rule with a strain "error" form is derived as the necessary condition for the optimum of a global remodeling criterion, suggesting that many of the local error-driven remodeling rules may have corresponding global optimization-based criteria. The global criterion proposed in the present study is a trade-off between the cost of metabolic growth and use, mathematically represented by the mass, and the cost of failure, mathematically represented by the total strain energy. The proposed global criterion is shown to be related to the optimality criteria methods of structural optimization by the equivalence of the model solution and the fully stressed solution for statically determinate structures. In related work, the global criterion is applied to simulate the strength recovery in bones with screw holes left behind after removal of fracture fixation plates. The results predicted by the model are shown to be in good agreement with experimental results, leading to the conclusion that load-bearing bones are structures with optimal shape and property for their function.     

草原革蜱生理龄期的观察

草原革蜱Dcrmacentor nuttalli分布于我国新疆、内蒙等地.苏联、蒙古亦有发现,国内外均巳证实是蜱媒斑点热的传播媒介.采用目测、解剖、切片三种方法,进行了生理龄期观察,目测法是观测蜱的腹部厚度变化,以体侧缘为纵轴线比较腹面与此线构成的距离,分成4个生理龄期;解剖法的主要指征是蜱中肠支囊体积和形态变化,马氏管内鸟嘌呤含量和支气管束等形态的变化;切片法的主要指征是中肠上皮细胞形态变化和细胞内血红蛋白颗粒和血红素的变化.三种方法比较,结果基本一致,误差在10％左右.草原革蜱在新疆阿勒泰地区自3月下旬至5月上旬绝大部分为Ⅱ、Ⅲ期的强壮蜱,此时正值当地牧放季节,处于活动高峰的媒介蜱,具有重要的流行病学意义.     

Estimation of Sample Size for Reference Interval Studies

The currently used criterion for sample size calculation in a reference interval study is not well stated and leads to imprecise control of the ratio in question. We propose a generalization of the criterion used to determine sufficient sample size in reference interval studies. The generalization allows better estimation of the required sample size when the reference interval estimation will be using a power transformation or is nonparametric. Bootstrap methods are presented to estimate sample sizes required by the generalized criterion. Simulation of several distributions both symmetric and positively skewed is presented to compare the sample size estimators. The new method is illustrated on a data set of plasma glucose values from a 50‐g oral glucose tolerance test. It is seen that the sample sizes calculated from the generalized criterion leads to more reliable control of the desired ratio.     

P-values for group sequential testing

Group sequential testing procedures are widely employed in long-termclinical trials. We discuss several methods for computing theP-value following a group sequential trial based on differentoutcome-space orderings. We use a criterion defined in thispaper to compare these methods and point out interesting differences.     

The calorimetric criterion for a two-state process revisited.

The "calorimetric criterion" is one of the important experimental approaches for determining whether protein folding is an "all-or-none" two-state transition (i.e., whether intermediates are present at equilibrium). The calorimetric criterion states that the equivalence of the "measured" calorimetric enthalpy change and the effective two-state van't Hoff enthalpy change demonstrates that there is a two-state transition. This paper addresses the essential question of whether the calorimetric criterion is a necessary and sufficient condition for a two-state process and shows that it is necessary but not sufficient by means of specific examples. Analysis of simple models indicates that the heat capacity curve, regardless of whether it originates from a two-state process or not, can always be decomposed in such a way that the calorimetric criterion is satisfied. Exact results for a three-state model and a homopolymer tetramer demonstrate that the deviation from the calorimetric criterion is not simply related to the population of intermediate states. Analysis of a three-helix bundle protein model, which has a two-state folding from a random coil to ordered (molten) globule, shows that the calorimetric criterion may not be satisfied if the standard linear interpolation of baselines (weighted or unweighted) is employed. A specific example also suggests that the more recently introduced deconvolution method is not necessarily better than the simple calorimetric criterion for distinguishing a two-state transition from a three-state transition. Although the calorimetric criterion is not a sufficient condition for a two-state process, it is likely to continue to be of practical utility, particularly when its results are shown to be consistent with those from other experimental methods.     

Prediction of Catalytic Residues Using the Variation of Stereochemical Properties

In this paper, we investigate a simple protein sequence conservation measure which takes amino acid similarity into account. Instead of grouping 20 amino acids into disjoint sets in previous methods, we consider ten overlapping classes. The method is based on the assumption that a column in a multiple sequence alignment is evolved from an identical column in the evolutionary history. Two ten-dimensional vectors are constructed for each position to denote frequencies of ten classes in a column and the corresponding hypothetical identical column. Then the cosine function of the angle between these two vectors is considered as a measure of divergence of stereochemical properties at this position. This divergence, combining with other conservation scores, is used as conservation measure of the column. Finally, we evaluate our methods by identifying catalytic sites, using rank analysis criterion and receiver operator characteristic analysis criterion.     

Genetic algorithm based two-mode clustering of metabolomics data

Metabolomics and other omics tools are generally characterized by large data sets with many variables obtained under different environmental conditions. Clustering methods and more specifically two-mode clustering methods are excellent tools for analyzing this type of data. Two-mode clustering methods allow for analysis of the behavior of subsets of metabolites under different experimental conditions. In addition, the results are easily visualized. In this paper we introduce a two-mode clustering method based on a genetic algorithm that uses a criterion that searches for homogeneous clusters. Furthermore we introduce a cluster stability criterion to validate the clusters and we provide an extended knee plot to select the optimal number of clusters in both experimental and metabolite modes. The genetic algorithm-based two-mode clustering gave biological relevant results when it was applied to two real life metabolomics data sets. It was, for instance, able to identify a catabolic pathway for growth on several of the carbon sources. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. J. A. Hageman and R. A. van den Berg contributed equally to this paper.     

Performance of criteria for selecting evolutionary models in phylogenetics: a comprehensive study based on simulated datasets

Background  

Explicit evolutionary models are required in maximum-likelihood and Bayesian inference, the two methods that are overwhelmingly used in phylogenetic studies of DNA sequence data. Appropriate selection of nucleotide substitution models is important because the use of incorrect models can mislead phylogenetic inference. To better understand the performance of different model-selection criteria, we used 33,600 simulated data sets to analyse the accuracy, precision, dissimilarity, and biases of the hierarchical likelihood-ratio test, Akaike information criterion, Bayesian information criterion, and decision theory.