Discriminating Different Classes of Biological Networks by Analyzing the Graphs Spectra Distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e. g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.

Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

The classification of texts has become a major endeavor with so much electronic material available, for it is an essential task in several applications, including search engines and information retrieval. There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies. (c) 2012 Elsevier B.V. All rights reserved.

Growth kinetics of CdS in germanium oxide glassy matrix

In the present work we revisit the size data of CdS microcrystals previously collected in the glassy matrix of Germanium oxide. The CdS clusters analyzed using electron microscopy images have shown a wurtzite structure. The mean average radius, dispersion and volume evaluated from the histograms showed good agreement for t(1/3), t(2/3) and t laws, respectively. We observed that the amount of microcrystals remains constant throughout the heat treatment process, as well as that the radii distribution has a lower limit and increases with heat treatment. The distribution of radii follows a distribution similar to the Lifshitz-Slyozov-Wagner distribution limited in the origin. Discussions led to the conclusion that the growth of CdS is a process that occurs after the fluctuating nucleation and coalescence phases. We then analyze the growth process, assuming that the evaporation is overcome by the precipitation rate, stabilizing all clusters with respect to dissolution back into the matrix. The problem was simplified neglecting anisotropy and the assuming a spherical shape for clusters and particles. The low interface tension was described in terms of an empirical potential barrier in the surface of the cluster. The growth dynamics developed considering that the number of clusters remains constant, and that the minimum size of these clusters grow with time, as the first order approximation showed a good agreement with the flaw. (C) 2012 Elsevier B.V. All rights reserved.

WEAR OF MATRIX OVERDENTURE ATTACHMENTS AFTER ONE TO EIGHT YEARS OF CLINICAL USE

Statement of problem. Matrices of unsplinted attachment systems are generally reported to be the weak component of implant overdentures, often requiring frequent maintenance. Clinical wear results in reduced retention of the prosthesis, requiring activation or renewal of the matrix to restore the initial level of retention. Purpose. The purpose of this retrospective study was to measure the wear of the matrix of a ball attachment after various periods of clinical wear. Material and methods. Seventy specimens of 3 groups of matrices of ball attachments that had been in use for mean periods of 12.3 months (1Y group, n=26), 39.0 months (3Y group, n=28) and 95.6 months (8Y group, n=16) were retrieved from 35 patients (2 specimens per patient) and measured on a coordinate measuring machine equipped with a touch trigger probe. Ten unused matrices were used as controls (CTRL group). The external and internal matrix diameters and deviations from circularity were measured. For the various time periods, the decreases in matrix thickness were calculated and compared with controls. Kruskal-Wallis 1-way ANOVA by ranks, followed by the Mann-Whitney post hoc tests, were conducted to test for differences in median values among groups (alpha=.05). Results. For the internal upper diameter of the matrices tested, the Kruskal-Wallis and Mann-Whitney tests revealed significant differences for the 3 groups compared to the controls. For group 1Y, a significant difference (P<.001) of the internal upper diameter was found compared to the CTRL group. Compared to the controls, the nonparametric analyses for groups 3Y and 8Y showed significant differences for the internal upper diameter (P<.001) and deviations from circularity (P<.001). For groups 1Y, 3Y and 8Y, matrix thickness losses were 07, 47 and 70 pm, respectively. Conclusions. Within the limitations of this study, it was observed that one year of clinical wear had limited effect on the ball attachment matrices. Three to 8 years of clinical use resulted in a significant decrease of matrix thickness, especially at the tip of the retentive lamellae. (J Prosthet Dent 2012;107:191-198)

Magnetic fabric of Araguainha complex impact structure (Central Brazil): Implications for deformation mechanisms and central uplift formation

The weakening mechanisms involved in the collapse of complex impact craters are controversial. The Araguainha impact crater, in Brazil, exposes a complex structure of 40 km in diameter, and is an excellent object to address this issue. Its core is dominated by granite. In addition to microstructural observations, magnetic studies reveal its internal fabric acquired during the collapse phase. All granite samples exhibit impact-related planar deformation features (PDFs) and planar fractures (PFs), which were overprinted by cataclasis. Cataclastic deformation has evolved from incipient brittle fracturing to the development of discrete shear bands in the center of the structure. Fracture planes are systematically decorated by tiny grains (<10 mu m) of magnetite and hematite, and the orientation of magnetic lineation and magnetic foliation obtained by the anisotropies of magnetic susceptibility (AMS) and anhysteretic remanence (AAR) are perfectly coaxial in all studied sites. Therefore, we could track the orientation of deformation features which are decorated by iron oxides using the AMS and AAR. The magnetic fabrics show a regular pattern at the borders of the central peak, with orientations consistent with the fabric of sediments at the crater's inner collar and complex in the center of the structure. Both the cataclastic flow revealed from microstructural observations and the structural pattern of the magnetic anisotropy match the predictions from numerical models of complex impact structures. The widespread occurrence of cataclasis in the central peak, and its orientations revealed by magnetic studies indicate that acoustic fluidization likely operates at all scales, including the mineral scales. The cataclastic flow made possible by acoustic fluidization results in an apparent plastic deformation at the macroscopic scale in the core. (C) 2012 Elsevier B.V. All rights reserved.