Bayesian tests on components of the compound symmetry covariance matrix

Complex dependency structures are often conditionally modeled, where random effects parameters are used to specify the natural heterogeneity in the population. When interest is focused on the dependency structure, inferences can be made from a complex covariance matrix using a marginal modeling approach. In this marginal modeling framework, testing covariance parameters is not a boundary problem. Bayesian tests on covariance parameter(s) of the compound symmetry structure are proposed assuming multivariate normally distributed observations. Innovative proper prior distributions are introduced for the covariance components such that the positive definiteness of the (compound symmetry) covariance matrix is ensured. Furthermore, it is shown that the proposed priors on the covariance parameters lead to a balanced Bayes factor, in case of testing an inequality constrained hypothesis. As an illustration, the proposed Bayes factor is used for testing (non-)invariant intra-class correlations across different group types (public and Catholic schools), using the 1982 High School and Beyond survey data

Programmable multimetallic linear nanoassemblies of ruthenium–DNA conjugates

A new ruthenium–DNA conjugates family was synthesized, made up of a ruthenium complex bound to one or two identical DNA strands of 14–58 nucleotides. The formation of controlled linear nanoassemblies containing one to seven ruthenium complexes is described

Bayes Factor Covariance Testing in Item Response Models

Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components. The posterior distribution of common covariance components is obtained in closed form by transforming latent responses with an orthogonal (Helmert) matrix. This posterior distribution is defined as a shifted-inverse-gamma, thereby introducing a default prior and a balanced prior distribution. Based on that, an MCMC algorithm is described to estimate all model parameters and to compute (fractional) Bayes factor tests. Simulation studies are used to show that the (fractional) Bayes factor tests have good properties for testing the underlying covariance structure of binary response data. The method is illustrated with two real data studies

Si photonic device uniformity improvement using wafer-scale location specific processing

We report two-fold improvement in Si photonic device uniformity over a 200mm SOI wafer through location specific processing. A within wafer thickness non-uniformity of 0.8nm yielding a grating fiber-coupler peak-wavelength non-uniformity of 1.8nm is achieved

Piecing together the problems in diagnosing low-level chromosomal mosaicism

Low-level somatic chromosomal mosaicism, which usually arises from post-zygotic errors, is a known cause of several well defined genetic syndromes and has been implicated in various multifactorial diseases. It is, however, not easy to diagnose, as various physical and technical factors complicate its identification

Complex Latent Variable Modeling in Educational Assessment

Bayesian item response theory models have been widely used in different research fields. They support measuring constructs and modeling relationships between constructs, while accounting for complex test situations (e.g., complex sampling designs, missing data, heterogenous population). Advantages of this flexible modeling framework together with powerful simulation-based estimation techniques are discussed. Furthermore, it is shown how the Bayes factor can be used to test relevant hypotheses in assessment using the College Basic Academic Subjects Examination (CBASE) data

Point Mutations of Two Arginine Residues in the Streptomyces R61 Dd-Peptidase

peer reviewedIncubation of the exocellular DD-carboxypeptidase/transpeptidase of Streptomyces R61 with phenylglyoxal resulted in a time-dependent decrease in the enzyme activity. This inactivation was demonstrated to be due to modification of the Arg-99 side chain. In consequence, the role of that residue was investigated by site-directed mutagenesis. Mutation of Arg-99 into leucine appeared to be highly detrimental to enzyme stability, reflecting a determining structural role for this residue. The conserved Arg-103 residue was also substituted by using site-directed mutagenesis. The modification to a serine residue yielded a stable enzyme, the catalytic properties of which were similar to those of the wild-type enzyme. Thus Arg-103, although strictly conserved or replaced by a lysine residue in most of the active-site penicillin-recognizing proteins, did not appear to fulfil any essential role in either the enzyme activity or structure