Intracavity Generation of Radially Polarized CO2 Laser Beams Based on a Simple Binary Dielectric Diffraction Grating

We present experimental results on the intracavity generation of radially polarized light by incorporation of a polarization-selective mirror in a CO2 -laser resonator. The selectivity is achieved with a simple binary dielectric diffraction grating etched in the backsurface of the mirror substrate. Very high polarization selectivity was achieved, and good agreement of simulation and experimental results is shown. The overall radial polarization purity of the generated laser beam was found to be higher than 90% .

A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints

Publication bias and related bias in meta-analysis is often examined by visually checking for asymmetry in funnel plots of treatment effect against its standard error. Formal statistical tests of funnel plot asymmetry have been proposed, but when applied to binary outcome data these can give false-positive rates that are higher than the nominal level in some situations (large treatment effects, or few events per trial, or all trials of similar sizes). We develop a modified linear regression test for funnel plot asymmetry based on the efficient score and its variance, Fisher's information. The performance of this test is compared to the other proposed tests in simulation analyses based on the characteristics of published controlled trials. When there is little or no between-trial heterogeneity, this modified test has a false-positive rate close to the nominal level while maintaining similar power to the original linear regression test ('Egger' test). When the degree of between-trial heterogeneity is large, none of the tests that have been proposed has uniformly good properties.

A Faster Circular Binary Segmentation Algorithm for the Analysis of Array CGH Data

Motivation: Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number (Olshen {\it et~al}, 2004). The algorithm tests for change-points using a maximal $t$-statistic with a permutation reference distribution to obtain the corresponding $p$-value. The number of computations required for the maximal test statistic is $O(N^2),$ where $N$ is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the need for a faster. algorithm. Results: We present a hybrid approach to obtain the $p$-value of the test statistic in linear time. We also introduce a rule for stopping early when there is strong evidence for the presence of a change. We show through simulations that the hybrid approach provides a substantial gain in speed with only a negligible loss in accuracy and that the stopping rule further increases speed. We also present the analysis of array CGH data from a breast cancer cell line to show the impact of the new approaches on the analysis of real data. Availability: An R (R Development Core Team, 2006) version of the CBS algorithm has been implemented in the ``DNAcopy'' package of the Bioconductor project (Gentleman {\it et~al}, 2004). The proposed hybrid method for the $p$-value is available in version 1.2.1 or higher and the stopping rule for declaring a change early is available in version 1.5.1 or higher.

Quantifying and Comparing the Accuracy of Binary Biomarkers When Predicting a Failure Time Outcome

The positive and negative predictive value are standard measures used to quantify the predictive accuracy of binary biomarkers when the outcome being predicted is also binary. When the biomarkers are instead being used to predict a failure time outcome, there is no standard way of quantifying predictive accuracy. We propose a natural extension of the traditional predictive values to accommodate censored survival data. We discuss not only quantifying predictive accuracy using these extended predictive values, but also rigorously comparing the accuracy of two biomarkers in terms of their predictive values. Using a marginal regression framework, we describe how to estimate differences in predictive accuracy and how to test whether the observed difference is statistically significant.

A BAYESIAN SHRINKAGE MODEL FOR INCOMPLETE LONGITUDINAL BINARY DATA WITH APPLICATION TO THE BREAST CANCER PREVENTION TRIAL

We consider inference in randomized studies, in which repeatedly measured outcomes may be informatively missing due to drop out. In this setting, it is well known that full data estimands are not identified unless unverified assumptions are imposed. We assume a non-future dependence model for the drop-out mechanism and posit an exponential tilt model that links non-identifiable and identifiable distributions. This model is indexed by non-identified parameters, which are assumed to have an informative prior distribution, elicited from subject-matter experts. Under this model, full data estimands are shown to be expressed as functionals of the distribution of the observed data. To avoid the curse of dimensionality, we model the distribution of the observed data using a Bayesian shrinkage model. In a simulation study, we compare our approach to a fully parametric and a fully saturated model for the distribution of the observed data. Our methodology is motivated and applied to data from the Breast Cancer Prevention Trial.

A unified approach to modeling multivariate binary data using copulas over partitions

Many seemingly disparate approaches for marginal modeling have been developed in recent years. We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the proposed copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts.

Oxidation of 3-hydroxyanthranilic acid to the phenoxazinone cinnabarinic acid by peroxyl radicals and by compound I of peroxidases or catalase

Since 3-hydroxyanthranilic acid (3HAA), an oxidation product of tryptophan metabolism, is a powerful radical scavenger [Christen, S., Peterhans, E., ; Stocker, R. (1990) Proc. Natl. Acad. Sci. U.S.A. 87, 2506], its reaction with peroxyl radicals was investigated further. Exposure to aqueous peroxyl radicals generated at constant rate under air from the thermolabile radical initiator 2,2'-azobis[2-amid-inopropane] hydrochloride (AAPH) resulted in rapid consumption of 3HAA with initial accumulation of its cyclic dimer, cinnabarinic acid (CA). The initial rate of formation of the phenoxazinone CA accounted for approximately 75% of the initial rate of oxidation of 3HAA, taking into account that 2 mol of 3HAA are required to form 1 mol of CA. Consumption of 3HAA under anaerobic conditions (where alkyl radicals are produced from AAPH) was considerably slower and did not result in detectable formation of CA. Addition of superoxide dismutase enhanced autoxidation of 3HAA as well as the initial rates of peroxyl radical-induced oxidation of 3HAA and formation of CA by approximately 40-50%, whereas inclusion of xanthine/xanthine oxidase decreased the rate of oxidation of 3HAA by approximately 50% and inhibited formation of CA almost completely, suggesting that superoxide anion radical (O2.-) was formed and reacted with reaction intermediate(s) to curtail formation of CA. Formation of CA was also observed when 3HAA was added to performed compound I of horseradish peroxidase (HRPO) or catalytic amounts of either HRPO, myeloperoxidase, or bovine liver catalase together with glucose/glucose oxidase.(ABSTRACT TRUNCATED AT 250 WORDS)

Infantile hypophosphatasia due to a new compound heterozygous TNSALP mutation - functional evidence for a hydrophobic side-chain?

BACKGROUND: Infantile hypophosphatasia (IH) is an inherited disorder characterized by defective bone mineralization and a deficiency of alkaline phosphatase activity. OBJECTIVE/DESIGN: The aim of the study was to evaluate a new compound heterozygous TNSALP mutation for its residual enzyme activity and localization of the comprised amino acid residues in a 3D-modeling. PATIENT: We report on a 4-week old girl with craniotabes, severe defects of ossification, and failure to thrive. Typical clinical features as low serum alkaline phosphatase, high serum calcium concentration, increased urinary calcium excretion, and nephrocalcinosis were observed. Vitamin D was withdrawn and the patient was started on calcitonin and hydrochlorothiazide. Nonetheless, the girl died at the age of 5 months from respiratory failure. RESULTS: Sequence analysis of the patient's TNSALP gene revealed two heterozygous mutations [c.653T>C (I201T), c.1171C>T (R374C)]. Transfection studies of the unique I201T variant in COS-7 cells yielded a mutant TNSALP protein with only a residual enzyme activity (3.7%) compared with wild-type, whereas the R374C variant was previously shown to reduce normal activity to 10.3%. 3D-modeling of the mutated enzyme showed that I201T resides in a region that does not belong to any known functional site. CONCLUSION: We note that I201, which has been conserved during evolution, is buried in a hydrophobic pocket and, therefore, the I>T-change should affect its functional properties. Residue R374C is located in the interface between monomers and it has been previously suggested that this mutation affects dimerization. These findings explain the patient's clinical picture and severe course.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

OBJECTIVES: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. METHODS: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. RESULTS: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. CONCLUSION: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.