A somatotype method for young boys

The purpose of this study was to determine the relationships between the three somatotype componets and selected structural, strength and motor performance characteristics of 12 year old boys. Zero-order correlations, first-order partials, and multiple correlations were computed in an effort to find the variables most highly related to each somatotype component. Although no single anthropometric measure had a correlation of sufficient magnitude with any somatotype component to justify prediction of the component, such prediction was possible by means of regression equations. Utilizing measures of standing height, body weight, and leg length, regression equations were developed which yielded somatotype ratings which were within the range of accuracy of experienced raters. Thus, a practical method of determining the somatotype of young boys was developed without the need for photography or subjective ratings.     

A survey of multiple sequence comparison methods

Multiple sequence comparison refers to the search for similarity in three or more sequences. This article presents a survey of the exhaustive (optimal) and heuristic (possibly sub-optimal) methods developed for the comparison of multiple macromolecular sequences. Emphasis is given to the different approaches of the heuristic methods. Four distance measures derived from information engineering and genetic studies are introduced for the comparison between two alignments of sequences. The use ofentropy, which plays a central role in information theory as measures of information, choice and uncertainty, is proposed as a simple measure for the evaluation of the optimality of an alignment in the absence of anya priori knowledge about the structures of the sequences being compared. This article also gives two examples of comparison between alternative alignments of the same set of 5SRNAs as obtained by several different heuristic methods.     

A comparison of gene region simulation methods

Background

Accurately modeling LD in simulations is essential to correctly evaluate new and existing association methods. At present, there has been minimal research comparing the quality of existing gene region simulation methods to produce LD structures similar to an existing gene region. Here we compare the ability of three approaches to accurately simulate the LD within a gene region: HapSim (2005), Hapgen (2009), and a minor extension to simple haplotype resampling.

Methodology/Principal Findings

In order to observe the variation and bias for each method, we compare the simulated pairwise LD measures and minor allele frequencies to the original HapMap data in an extensive simulation study. When possible, we also evaluate the effects of changing parameters.HapSim produces samples of haplotypes with lower LD, on average, compared to the original haplotype set while both our resampling method and Hapgen do not introduce this bias. The variation introduced across the replicates by our resampling method is quite small and may not provide enough sampling variability to make a generalizable simulation study.

Conclusion

We recommend using Hapgen to simulate replicate haplotypes from a gene region. Hapgen produces moderate sampling variation between the replicates while retaining the overall unique LD structure of the gene region.     

A comparison of different methods of half-diallel analysis

Summary A comparison among various forms of half-diallel analysis was made. The different half-diallel techniques used were: Griffing's model I, method 2 and 4, Morley-Jones' model; Walters and Morton's model, and Gardner and Eberhart's model. All these methods of diallel analysis were found to be interrelated. However, as the Gardner and Eberhart's model partitioned heterosis into different components as well as gave information about combining ability, this method had certainly some advantages over the others. The results further indicated the possibility of dominance variance being confounded with the additive variance of general combining ability.     

A comparison of methods for assessing yeast viability

Summary Eight different methods were used to assess the cell viability of four strains of Saccharomyces. Staining with Mg-ANS, primuline yellow, FITC and methylene blue gave a good index of yeast cell viability. The standard plate count technique and microcolony formation also gave a good measure of cell viability. Fluorescent staining with acridine orange was the least useful of the methods tested. INT dye reduction gave a good index of respiring cells depending upon the yeast strain tested.     

A comparison of methods for predicting vegetation type

Predictive modeling of vegetation patterns has wide application in vegetation science. In this paper I discuss three methods of predictive modeling using data from the alpine treeline ecotone as a case study. The study area is a portion of Glacier National Park, Montana. Parametric general linear models (GLM), artificial neural networks (ANN) and classification tree (CT) methods of predicting vegetation type are compared to determine the relative strength of each predictive approach and how they may be used in concert to increase understanding of important vegetation – environment relations. For each predictive method, vegetation type within the alpine treeline ecotone is predicted using a suite of environmental indicator variables including elevation, moisture potential, solar radiation potential, snow potential index, and disturbance history. Results from each of the predictive methods are compared against the real vegetation types to determine the relative accuracy of the methods.When the entire data field is examined (i.e., not evaluated by smaller spatial aggregates of data) the ANN procedure produces the most accurate predictions (=0.571); the CT predictions are the least accurate (=0.351). The predicted patterns of vegetation on the landscape are considerably different using the three methods. The GLM and CT methods produce large contiguous swaths of vegetation types throughout the study area, whereas the ANN method produces patterns with much more heterogeneity and smaller patches.When predictions are compared to reality at catchment scale, it becomes evident that the accuracy of each method varies depending upon the specific situation. The ANN procedure remains the most accurate method in the majority of the catchments, but both the GLM and PCT produce the most accurate classifications in at least one basin each.The variability in predictive ability of the three methods tested here indicates that there may not be a single best predictive method. Rather it may be important to use a suite of predictive models to help understand the environment – vegetation relationships. The ability to use multiple predictive methods to determine which spatial subunits of a landscape are outliers is important when identifying locations useful for climate change monitoring studies.     

A comparison of methods for adaptive treatment selection

Traditionally drug development is generally divided into three phases which have different aims and objectives. Recently so-called adaptive seamless designs that allow combination of the objectives of different development phases into a single trial have gained much interest. Adaptive trials combining treatment selection typical for Phase II and confirmation of efficacy as in Phase III are referred to as adaptive seamless Phase II/III designs and are considered in this paper. We compared four methods for adaptive treatment selection, namely the classical Dunnett test, an adaptive version of the Dunnett test based on the conditional error approach, the combination test approach, and an approach within the classical group-sequential framework. The latter two approaches have only recently been published. In a simulation study we found that no one method dominates the others in terms of power apart from the adaptive Dunnett test that dominates the classical Dunnett by construction. Furthermore, scenarios under which one approach outperforms others are described.     

A comparison of methods for estimating mean fecundity

Four computational methods for estimating mean fecundity are compared by Monte Carlo simulation. One of the four methods is the simple expedient of estimating fecundity at sample mean length, a method known to be downwardly biassed. The Monte Carlo study shows that the other three methods reduce bias and provide worthwhile efficiency gains. For small samples, the most efficient of the four methods is a 'bias adjustment', proposed here, that uses easily calculated sample statistics. For large samples, a numerical integration method has the highest efficiency. The fourth method, a 'direct summation' procedure which can be done easily in many statistical or spreadsheet programs, performs well for all sample sizes.     

A comparison of four pair-wise sequence alignment methods

Protein sequence alignment has become an essential task in modern molecular biology research. A number of alignment techniques have been documented in literature and their corresponding tools are made available as freeware and commercial software. The choice and use of these tools for sequence alignment through the complete interpretation of alignment results is often considered non-trivial by end-users with limited skill in Bioinformatics algorithm development. Here, we discuss the comparison of sequence alignment techniques based on dynamic programming (N-W, S-W) and heuristics (LFASTA, BL2SEQ) for four sets of sequence data towards an educational purpose. The analysis suggests that heuristics based methods are faster than dynamic programming methods in alignment speed.     

A comparison of methods for determining compartmental analysis parameters

The traditional method for determining compartmental analysis parameters relies on a visual selection of data points to be used for regression of data from each cellular compartment. This method is appropriate when the compartments are kinetically discrete and are easily discernible. However, where treatment effects on compartment parameters are being evaluated, a more objective method for determining initial parameters is desirable.

Three methods were examined for determining initial isotopic contents and half-times of ⁸⁶Rb elution from cellular compartments using theoretical data with known parameters. Experimental data from roots of Douglas fir (Pseudotsuga menziesii [Mirb.] Franco) and barley (Hordeum vulgare L.) intact seedlings were also used. The three methods were a visually assisted, linear regression on data of semilog plot of isotope elution versus time, a microcomputer-assisted, linear regression on semilog plot where maximization of the square of the correlation coefficient (r²) was the criterion to determine data points needed for each regression and a mainframe computer-assisted, direct nonlinear regression on elution data using a model of the sum of three exponential decay functions. The visual method resulted in the least accurate estimates of compartmental analysis parameters. The microcomputer-assisted and nonlinear regression methods calculated the parameters equally well.

     

A comparison of molecular typing methods for Moraxella catarrhalis

AIMS: Three molecular typing techniques were examined to determine which method was the most discriminatory in order to perform epidemiological typing of Moraxella catarrhalis. METHODS AND RESULTS: Twenty-five Mor. catarrhalis isolates obtained from nasopharyngeal aspirates collected from Aboriginal and non-Aboriginal children were subjected to random amplified polymorphic DNA (RAPD) analysis, automated ribotyping and pulsed field gel electrophoresis (PFGE). RAPD analysis determined two Mor. catarrhalis types, automated ribotyping with PstI determined four Mor. catarrhalis ribogroups and PFGE analysis with NotI determined 21 pulse field groups within the 25 isolates examined. CONCLUSIONS: Analysis of discrimination index and typeability demonstrated that PFGE is the most discriminatory method for typing Mor. catarrhalis. SIGNIFICANCE AND IMPACT OF THE STUDY: This study confirms that PFGE is the most appropriate molecular tool for the epidemiological study of Mor. catarrhalis.     

A comparison of methods employed to evaluate antioxidant capabilities

Three different methodologies frequently employed to evaluate the indexes that report the antioxidant capabilities of pure compounds and/or complex mixtures of antioxidants are applied to a series of mono- and polyphenols, as well as to two wine (red and white) samples. These methodologies are based on the bleaching of a stable radical, the effect of the additive upon luminol chemiluminescence induced by peroxyl radicals, and the effect of the additive upon the bleaching of the fluorescence from a dye molecule. Widely different responses are obtained from the different methodologies. These differences are interpreted in terms of the different factors (stoichiometric factors and/or reactivities) that determines the indexes evaluated by these different methodologies.     

A comparison of five methods for assaying bacterial hydrophobicity

Five methods for assaying bacterial surface hydrophobicity, namely, bacterial adherence to hydrocarbons, salt aggregation, hydrophobic interaction chromatography, adhesion to polystyrene and latex particle agglutination were used to compare the hydrophobic surface properties of Escherichia coli, Acinetobacter calcoaceticus, Staphylococcus aureus and Streptococcus mitis. Two strains of A. calcoaceticus, including RAG-1, gave strong positive results by all five methods. S. mitis gave weak or negative results by all methods. The results for the other bacteria varied with the method. We conclude that reliance on one method for such tests is inadequate.