Coupling of Two Motor Proteins: a New Motor Can Move Faster

We study the effect of a coupling between two motor domains in highly-processive motor protein complexes. A simple stochastic discrete model, in which the two parts of the protein molecule interact through some energy potential, is presented. The exact analytical solutions for the dynamic properties of the combined motor species, such as the velocity and dispersion, are derived in terms of the properties of free individual motor domains and the interaction potential. It is shown that the coupling between the motor domains can create a more efficient motor protein that can move faster than individual particles. The results are applied to analyze the motion of helicase RecBCD molecules

A Comparison of Factor Rotation Methods for Dichotomous Data

Exploratory factor analysis (EFA) is frequently used in the social sciences and is a common component in many validity studies. A core aspect of EFA is the determination of which observed indicator variables are associated with which latent factors through the use of factor loadings. Loadings are initially extracted using an algorithm, such as maximum likelihood or weighted least squares, and then transformed - or rotated - to make them more interpretable. There are a number of rotational techniques available to the researcher making use of EFA. Prior work has discussed the advantages of a number of these criteria from a theoretical perspective, but few previous studies compare their performance across a broad range of conditions. This simulation study compared eight factor rotation criteria in terms of how well they were able to group dichotomous indicator variables correctly on the same factor, order the indicators by the magnitude of the factor loadings (identifying those indicators that were most strongly associated with the factors) and estimate the inter-factor correlations. Results reveal a mixed pattern of performance among the various rotations with the orthogonal Equamax consistently near the top in terms of correctly grouping and ordering indicator variables, and the orthogonal Facparsim performing well with more observed indicators. Advice regarding possible rotations to use for researchers conducting EFA with dichotomous indicators is provided

Regularization Methods for Fitting Linear Models with Small Sample Sizes: Fitting the Lasso Estimator using R

Researchers and data analysts are sometimes faced with the problem of very small samples, where the number of variables approaches or exceeds the overall sample size; i.e. high dimensional data. In such cases, standard statistical models such as regression or analysis of variance cannot be used, either because the resulting parameter estimates exhibit very high variance and can therefore not be trusted, or because the statistical algorithm cannot converge on parameter estimates at all. There exist an alternative set of model estimation procedures, known collectively as regularization methods, which can be used in such circumstances, and which have been shown through simulation research to yield accurate parameter estimates. The purpose of this paper is to describe, for those unfamiliar with them, the most popular of these regularization methods, the lasso, and to demonstrate its use on an actual high dimensional dataset involving adults with autism, using the R software language. Results of analyses involving relating measures of executive functioning with a full scale intelligence test score are presented, and implications of using these models are discussed. Accessed 4,969 times on https://pareonline.net from May 08, 2016 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Third Movement

Sweat popped from his forehead, coursed down the crevices to the light stubble of his beard, gathered weight, itched its way around the promontory of chin, surged in rivulets down the leathery neck, collected in a pool at the base of his throat, spilled over on the breast, was absorbed by clothing, became a source of future irritation-unnoticed at the moment. His tommy negligently tucked in the crook of his right arm, some hundred and fifty pounds of equipment stowed about his person, weight pressed into the cable-guard, he stood with feet spread wide on the loading ramp of the transport. With lackluster eyes and vacuous expression, he waited for the men ahead to move-waited. This was H hour minus twelve

Using Exploratory Factor Analysis for Locating Invariant Referents in Factor Invariance Studies

Model identification in multi-group confirmatory factor analysis (MCFA) requires an equality constraint of referent variables across groups. Invariance assumption violations make it difficult to locate parameters that actually differ. Suggested procedures for locating invariant referents are cumbersome, complex, and provide imperfect results. Exploratory factor analysis (EFA) may be an alternative because of its ease of use, yet empirical evaluation of its effectiveness is lacking. EFAs accuracy for distinguishing invariant from non-invariant referents was examined

Comparing Factor Loadings in Exploratory Factor Analysis: A New Randomization Test

Factorial invariance testing requires a referent loading to be constrained equal across groups. This study introduces a randomization test for comparing group exploratory factor analysis loadings so as to identify an invariant referent. Results show that it maintains the Type I error rate while providing adequate power under most conditions

Reconstruction of deglacial sea surface temperatures in the tropical Pacific from selective analysis of a fossil coral

The Sr/Ca of coral skeletons demonstrates potential as an indicator of sea surface temperatures (SSTs). However, the glacial-interglacial SST ranges predicted from Sr/Ca of fossil corals are usually higher than from other marine proxies. We observed infilling of secondary aragonite, characterised by high Sr/Ca ratios, along intraskeletal pores of a fossil coral from Papua New Guinea that grew during the penultimate deglaciation (130 +/- 2 ka). Selective microanalysis of unaltered areas of the fossil coral indicates that SSTs at similar to 130 ka were &lt;= 1 degrees C cooler than at present in contrast with bulk measurements ( combining infilled and unaltered areas) which indicate a difference of 6-7 degrees C. The analysis of unaltered areas of fossil skeletons by microprobe techniques may offer a route to more accurate reconstruction of past SSTs.</p