Acellular dermal matrix as a barrier in guided bone regeneration: a clinical, radiographic and histomorphometric study in dogs

Objectives The purpose of this study was to evaluate the effectiveness of the acellular dermal matrix (ADM) as a membrane for guided bone regeneration (GBR), in comparison with a bioabsorbable membrane. Material and methods In seven dogs, the mandibular pre-molars were extracted. After 8 weeks, one bone defect was surgically created bilaterally and the GBR was performed. Each side was randomly assigned to the control group (CG: bioabsorbable membrane made of glycolide and lactide copolymer) or the test group (TG: ADM as a membrane). Immediately following GBR, standardized digital X-ray radiographs were taken, and were repeated at 8 and 16 weeks post-operatively. Before the GBR and euthanasia, clinical measurements of the width and thickness of the keratinized tissue (WKT and TKT, respectively) were performed. One animal was excluded from the study due to complications in the TG during wound healing; therefore, six dogs remained in the sample. The dogs were sacrificed 16 weeks following GBR, and a histomorphometric analysis was performed. Area measurements of new tissue and new bone, and linear measurements of bone height were performed. Results Post-operative healing of the CG was uneventful. In the TG membrane was exposed in two animals, and one of them was excluded from the sample. There were no statistically significant differences between the groups for any histomorphometric measurement. Clinically, both groups showed an increase in the TKT and a reduction in the WKT. Radiographically, an image suggestive of new bone formation could be observed in both groups at 8 and 16 weeks following GBR. Conclusion ADM acted as a barrier in GBR, with clinical, radiographic and histomorphometric results similar to those obtained with the bioabsorbable membrane. To cite this article:Borges GJ, Novaes AB Jr, de Moraes Grisi MF, Palioto DB, Taba M Jr, de Souza SLS. Acellular dermal matrix as a barrier in guided bone regeneration: a clinical, radiographic and histomorphometric study in dogs.Clin. Oral Impl. Res. 20, 2009; 1105-1115.

On S-matrix factorization of the Landau-Lifshitz model

We consider the three-particle scattering S-matrix for the Landau-Lifshitz model by directly computing the set of the Feynman diagrams up to the second order. We show, following the analogous computations for the non-linear Schrdinger model [1, 2], that the three-particle S-matrix is factorizable in the first non-trivial order.

Hyperfine and magnetic properties of Fe-Cu clusters and Fe precipitates embedded in a Cu matrix

Using the first-principles real-space linear muffin-tin orbital method within the atomic sphere approximation (RS-LMTO-ASA) we study hyperfine and local magnetic properties of substituted pure Fe and Fe-Cu clusters in an fcc Cu matrix. Spin and orbital contributions to magnetic moments, hyperfine fields and the Mossbauer isomer shifts at the Fe sites in Fe precipitates and Fe-Cu alloy clusters of sizes up to 60 Fe atoms embedded in the Cu matrix are calculated and the influence of the local environment on these properties is discussed.

Augmented Lagrangian methods under the constant positive linear dependence constraint qualification

Two Augmented Lagrangian algorithms for solving KKT systems are introduced. The algorithms differ in the way in which penalty parameters are updated. Possibly infeasible accumulation points are characterized. It is proved that feasible limit points that satisfy the Constant Positive Linear Dependence constraint qualification are KKT solutions. Boundedness of the penalty parameters is proved under suitable assumptions. Numerical experiments are presented.

Leverage analysis for linear mixed models

We consider a generalized leverage matrix useful for the identification of influential units and observations in linear mixed models and show how a decomposition of this matrix may be employed to identify high leverage points for both the marginal fitted values and the random effect component of the conditional fitted values. We illustrate the different uses of the two components of the decomposition with a simulated example as well as with a real data set.

Improved testing inference in mixed linear models

Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Often, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test, and also to a test obtained from a modified profile likelihood function. Our results generalize those in [Zucker, D.M., Lieberman, O., Manor, O., 2000. Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood. Journal of the Royal Statistical Society B, 62,827-838] by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report simulation results which show that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presented and discussed. (C) 2008 Elsevier B.V. All rights reserved.

Covariance matrix formula for Birnbaum-Saunders regression models

The Birnbaum-Saunders regression model is commonly used in reliability studies. We derive a simple matrix formula for second-order covariances of maximum-likelihood estimators in this class of models. The formula is quite suitable for computer implementation, since it involves only simple operations on matrices and vectors. Some simulation results show that the second-order covariances can be quite pronounced in small to moderate sample sizes. We also present empirical applications.

On Approximate KKT Condition and its Extension to Continuous Variational Inequalities

In this work, we introduce a necessary sequential Approximate-Karush-Kuhn-Tucker (AKKT) condition for a point to be a solution of a continuous variational inequality, and we prove its relation with the Approximate Gradient Projection condition (AGP) of Garciga-Otero and Svaiter. We also prove that a slight variation of the AKKT condition is sufficient for a convex problem, either for variational inequalities or optimization. Sequential necessary conditions are more suitable to iterative methods than usual punctual conditions relying on constraint qualifications. The AKKT property holds at a solution independently of the fulfillment of a constraint qualification, but when a weak one holds, we can guarantee the validity of the KKT conditions.