Flap vs. ""flapless"" surgical approach at immediate implants: a histomorphometric study in dogs

Aim To compare the remodeling of the alveolar process at implants installed immediately into extraction sockets by applying a flap or a ""flapless"" surgical approach in a dog model. Material and methods Implants were installed immediately into the distal alveoli of the second mandibular premolars of six Labrador dogs. In one side of the mandible, a full-thickness mucoperiosteal flap was elevated (control site), while contra-laterally, the mucosa was gently dislocated, but not elevated (test site) to disclose the alveolar crest. After 4 months of healing, the animals were sacrificed, ground sections were obtained and a histomorphometric analysis was performed. Results After 4 months of healing, all implants were integrated (n=6). Both at the test and at the control sites, bone resorption occurred with similar outcomes. The buccal bony crest resorption was 1.7 and 1.5 mm at the control and the test sites, respectively. Conclusions ""Flapless"" implant placement into extraction sockets did not result in the prevention of alveolar bone resorption and did not affect the dimensional changes of the alveolar process following tooth extraction when compared with the usual placement of implants raising mucoperiosteal flaps. To cite this article:Caneva M, Botticelli D, Salata LA, Souza SLS, Bressan E, Lang NP. Flap vs. ""flapless"" surgical approach at immediate implants: a histomorphometric study in dogs.Clin. Oral Impl. Res. 21, 2010; 1314-1319.doi: 10.1111/j.1600-0501.2009.01959.x.

Nitrogen fluxes from treefrogs to tank epiphytic bromeliads: an isotopic and physiological approach

Diverse invertebrate and vertebrate species live in association with plants of the large Neotropical family Bromeliaceae. Although previous studies have assumed that debris of associated organisms improves plant nutrition, so far little evidence supports this assumption. In this study we used isotopic ((15)N) and physiological methods to investigate if the treefrog Scinax hayii, which uses the tank epiphytic bromeliad Vriesea bituminosa as a diurnal shelter, contributes to host plant nutrition. In the field, bromeliads with frogs had higher stable N isotopic composition (delta(15)N) values than those without frogs. Similar results were obtained from a controlled greenhouse experiment. Linear mixing models showed that frog feces and dead termites used to simulate insects that eventually fall inside the bromeliad tank contributed, respectively, 27.7% (+/- 0.07 SE) and 49.6% (+/- 0.50 SE) of the total N of V. bituminosa. Net photosynthetic rate was higher in plants that received feces and termites than in controls; however, this effect was only detected in the rainy, but not in the dry season. These results demonstrate for the first time that vertebrates contribute to bromeliad nutrition, and that this benefit is seasonally restricted. Since amphibian-bromeliad associations occur in diverse habitats in South and Central America, this mechanism for deriving nutrients may be important in bromeliad systems throughout the Neotropics.

A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.

Regression Models with Heteroscedasticity using Bayesian Approach

In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. Applying these methodologies, we analyze a Colombian tallness data set to find differences that can be explained by socioeconomic conditions. We also present some theoretical and empirical results concerning the two models. From this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. We also observe that the convergence of the Gibbs sampling chain used in the Markov Chain Monte Carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

Distinguishing links up to link-homotopy by algebraic methods

In this paper we provide a complete algebraic invariant of link-homotopy, that is, an algebraic invariant that distinguishes two links if and only if they are link-homotopic. The paper establishes a connection between the ""peripheral structures"" approach to link-homotopy taken by Milnor, Levine and others, and the string link action approach taken by Habegger and Lin. (C) 2009 Elsevier B.V. All rights reserved.

The constrained compartmentalized knapsack problem: mathematical models and solution methods

The constrained compartmentalized knapsack problem can be seen as an extension of the constrained knapsack problem. However, the items are grouped into different classes so that the overall knapsack has to be divided into compartments, and each compartment is loaded with items from the same class. Moreover, building a compartment incurs a fixed cost and a fixed loss of the capacity in the original knapsack, and the compartments are lower and upper bounded. The objective is to maximize the total value of the items loaded in the overall knapsack minus the cost of the compartments. This problem has been formulated as an integer non-linear program, and in this paper, we reformulate the non-linear model as an integer linear master problem with a large number of variables. Some heuristics based on the solution of the restricted master problem are investigated. A new and more compact integer linear model is also presented, which can be solved by a branch-and-bound commercial solver that found most of the optimal solutions for the constrained compartmentalized knapsack problem. On the other hand, heuristics provide good solutions with low computational effort. (C) 2011 Elsevier BM. All rights reserved.

A flexible model for survival data with a cure rate: a Bayesian approach

In this paper we deal with a Bayesian analysis for right-censored survival data suitable for populations with a cure rate. We consider a cure rate model based on the negative binomial distribution, encompassing as a special case the promotion time cure model. Bayesian analysis is based on Markov chain Monte Carlo (MCMC) methods. We also present some discussion on model selection and an illustration with a real dataset.

Black-box decomposition approach for computational hemodynamics: One-dimensional models

Increasing efforts exist in integrating different levels of detail in models of the cardiovascular system. For instance, one-dimensional representations are employed to model the systemic circulation. In this context, effective and black-box-type decomposition strategies for one-dimensional networks are needed, so as to: (i) employ domain decomposition strategies for large systemic models (1D-1D coupling) and (ii) provide the conceptual basis for dimensionally-heterogeneous representations (1D-3D coupling, among various possibilities). The strategy proposed in this article works for both of these two scenarios, though the several applications shown to illustrate its performance focus on the 1D-1D coupling case. A one-dimensional network is decomposed in such a way that each coupling point connects two (and not more) of the sub-networks. At each of the M connection points two unknowns are defined: the flow rate and pressure. These 2M unknowns are determined by 2M equations, since each sub-network provides one (non-linear) equation per coupling point. It is shown how to build the 2M x 2M non-linear system with arbitrary and independent choice of boundary conditions for each of the sub-networks. The idea is then to solve this non-linear system until convergence, which guarantees strong coupling of the complete network. In other words, if the non-linear solver converges at each time step, the solution coincides with what would be obtained by monolithically modeling the whole network. The decomposition thus imposes no stability restriction on the choice of the time step size. Effective iterative strategies for the non-linear system that preserve the black-box character of the decomposition are then explored. Several variants of matrix-free Broyden`s and Newton-GMRES algorithms are assessed as numerical solvers by comparing their performance on sub-critical wave propagation problems which range from academic test cases to realistic cardiovascular applications. A specific variant of Broyden`s algorithm is identified and recommended on the basis of its computer cost and reliability. (C) 2010 Elsevier B.V. All rights reserved.

A hands-on approach for fitting long-term survival models under the GAMLSS framework

In many data sets from clinical studies there are patients insusceptible to the occurrence of the event of interest. Survival models which ignore this fact are generally inadequate. The main goal of this paper is to describe an application of the generalized additive models for location, scale, and shape (GAMLSS) framework to the fitting of long-term survival models. in this work the number of competing causes of the event of interest follows the negative binomial distribution. In this way, some well known models found in the literature are characterized as particular cases of our proposal. The model is conveniently parameterized in terms of the cured fraction, which is then linked to covariates. We explore the use of the gamlss package in R as a powerful tool for inference in long-term survival models. The procedure is illustrated with a numerical example. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Near threshold vibrational excitation of molecules by positron impact: A projection operator approach

We report vibrational excitation (v(i) = 0 -> v(f) = 1) cross-sections for positron scattering by H(2) and model calculations for the (v(i) = 0 -> v(f) = 1) excitation of the C-C symmetric stretch mode of C(2)H(2). The Feshbach projection operator formalism was employed to vibrationally resolve the fixed-nuclei phase shifts obtained with the Schwinger multichannel method. The near threshold behavior of H(2) and C(2)H(2) significantly differ in the sense that no low lying singularity (either virtual or bound state) was found for the former, while a e(+)-acetylene virtual state was found at the equilibrium geometry (this virtual state becomes a bound state upon stretching the molecule). For C(2)H(2), we also performed model calculations comparing excitation cross-sections arising from virtual (-i kappa(0)) and bound (+i kappa(0)) states symmetrically located around the origin of the complex momentum plane (i.e. having the same kappa(0)). The virtual state is seen to significantly couple to vibrations, and similar cross-sections were obtained for shallow bound and virtual states. (c) 2007 Elsevier B.V. All rights reserved.

A structure-dynamic approach to cortical organization: Number of paths and accessibility

A structure-dynamic approach to cortical systems is reported which is based on the number of paths and the accessibility of each node. The latter measurement is obtained by performing self-avoiding random walks in the respective networks, so as to simulate dynamics, and then calculating the entropies of the transition probabilities for walks starting from each node. Cortical networks of three species, namely cat, macaque and humans, are studied considering structural and dynamical aspects. It is verified that the human cortical network presents the highest accessibility and number of paths (in terms of z-scores). The correlation between the number of paths and accessibility is also investigated as a mean to quantify the level of independence between paths connecting pairs of nodes in cortical networks. By comparing the cortical networks of cat, macaque and humans, it is verified that the human cortical network tends to present the largest number of independent paths of length larger than four. These results suggest that the human cortical network is potentially the most resilient to brain injures. (C) 2009 Elsevier B.V. All rights reserved.

A pattern recognition approach to complex networks

Complex networks exist in many areas of science such as biology, neuroscience, engineering, and sociology. The growing development of this area has led to the introduction of several topological and dynamical measurements, which describe and quantify the structure of networks. Such characterization is essential not only for the modeling of real systems but also for the study of dynamic processes that may take place in them. However, it is not easy to use several measurements for the analysis of complex networks, due to the correlation between them and the difficulty of their visualization. To overcome these limitations, we propose an effective and comprehensive approach for the analysis of complex networks, which allows the visualization of several measurements in a few projections that contain the largest data variance and the classification of networks into three levels of detail, vertices, communities, and the global topology. We also demonstrate the efficiency and the universality of the proposed methods in a series of real-world networks in the three levels.

Structural effects and hydrogen bonds on N,N `-di(methoxycarbonylsulfenyl)urea, [CH(3)OC(O)SNH](2)CO, studied by experimental and theoretical methods

Pure N,N`-di(methoxycarbonylsulfenyl)urea, [CH(3)OC(O)SNH](2)CO, is quantitatively prepared by the hydrolysis reaction of CH(3)OC(O)SNCO and characterized by (1)H NMR, GC-MS and FTIR spectroscopy techniques. Structural and conformational properties are analyzed using a combined approach with data obtained from X-ray diffraction, vibrational spectra and theoretical calculation methods. The IR and Raman spectra for normal and deuterated species are reported. The crystal structure of [CH(3)OC(O)SNH](2)CO was determined by X-ray diffraction methods. The substance crystallizes in the orthorhombic P2(1)2(1)2 space group with a = 9.524(2), b = 12.003(1), c = 4.481 (1) angstrom, and Z = 2 moieties in the unit cell. The molecule is sited on a twofold crystallographic axis (C(2)) parallel to c and shows the anti-anti conformation (S-N single bonds antiperiplanar with respect to the opposite C-N single bonds in sulfenyl-urea-sic group). Neighboring molecules are arranged in a chain motif that extends along the C(2)-axis and is held by bifurcated NH center dot center dot center dot O center dot center dot center dot HN intermolecular bonds. A local planar symmetry is observed in the crystal for the central -SN(H)C(O)N(H)S- skeleton. Experimental and calculated data allow to trace this structural feature to the occurrence of N-H center dot center dot center dot O=C hydrogen bonding interactions. Calculated vibrational and structural properties are in good agreement with the experimentally determined features. (C) 2008 Elsevier B.V. All rights reserved.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.