On 3D Reconstruction of Porous Media by Using Spatial Correlation Functions

The challenging process of 3D porous media reconstruction from a single 2D image is investigated in this paper. The reconstruction of the 3D model is based on the statistical information derived from a 2D thin image of the material, by applying a spatial correlation function. For the first time, this paper reviews the commonly used auto-correlation functions for material characterization and discusses their properties making them useful for 3D porous media reconstruction. A set of experiments is conducted in order to analyze the reconstruction capabilities of the studied correlation functions, while some useful conclusions are drawn. Finally, by taking into account the reconstruction performance of the existed correlation functions, some desirable properties that need to be satisfied by an ideal correlation function towards the improvement of the reconstruction accuracy are determined

Thyroid hormones and peripheral nerve regeneration

Peripheral nerve regeneration is a unique process in which cellular rather than tissue response is involved. Depending on the extent and proximity of the lesion and the age and type of the neuronal soma, the cell body may either initiate a reparative response or may die. Microsurgical intervention may alter the prognosis after a peripheral nerve injury but to a certain extent. By altering the biochemical microenvironment of the neuron, we can increase the proportion of neurons that survive the injury and initiate the reparative response. Thyroid hormone critically regulates tissue growth and differentiation and plays a crucial role during organ development. Furthermore, recent research has provided new insight into thyroid hormone cellular action. Thyroid hormone regulates stress response intracellular signaling and targets molecules important for cytoskeletal stability and cell integrity. Changes in thyroid hormone signaling occur in nerve and other tissues, with important physiological consequences. The interest in thyroid hormone in the context of nerve regeneration has recently been revived. © 2013 Ioannis D. Papakostas and George A. Macheras

Efficacy of the sequential integration of psychotherapy and pharmacotherapy in major depressive disorder: A preliminary meta-analysis.

BACKGROUND: Prevention of relapse and recurrence represents an important task in the successful treatment of major depressive disorder (MDD). The aim of this meta-analysis was to examine the efficacy of the sequential integration of psychotherapy and pharmacotherapy in reducing the risk of relapse and recurrence in MDD. METHOD: Keyword searches were conducted in Medline, EMBASE, PsycINFO and the Cochrane Library from inception of each database to December 2008. Randomized controlled trials examining the efficacy of the administration of psychotherapy after successful response to acute-phase pharmacotherapy in the treatment of adults with MDD were considered for inclusion in the meta-analysis. RESULTS: Eight high-quality studies with 442 patients in a sequential treatment arm and 433 in a control treatment arm were included. The pooled risk ratio (RR) for relapse/recurrence was 0.797 [95% confidence interval (CI) 0.659-0.964] according to the random-effects model, suggesting a relative advantage in preventing relapse/recurrence for the sequential administration of treatments compared with control conditions. Performing subgroup analyses, we found a trend favoring psychotherapy during continuation of antidepressant drugs compared to antidepressants or treatment as usual (RR 0.842, 95% CI 0.674-1.051). Patients randomized to psychotherapy while antidepressants were discontinued were significantly less likely to experience relapse/recurrence compared to controls (RR 0.650, 95% CI 0.463-0.912). CONCLUSIONS: We found evidence that the sequential integration of psychotherapy and pharmacotherapy is a viable strategy for preventing relapse and recurrence in MDD. In addition, our findings suggest that discontinuation of antidepressant drugs may be feasible when psychotherapy is provided

Particle Swarm Optimization approach for fuzzy cognitive maps applied to autism classification

The task of classification using intelligent methods and learning algorithms is a difficult task leading the research community on finding new classifications techniques to solve it. In this work, a new approach based on particle swarm optimization (PSO) clustering is proposed to perform the fuzzy cognitive map learning for classification performance. Fuzzy cognitive map (FCM) is a simple, but also powerful computational intelligent technique which is used for the adoption of the human knowledge and/or historical data, into a simple mathematical model for system modeling and analysis. The aim of this study is to investigate a new classification algorithm for the autism disorder problem by integrating the Particle Swarm Optimization method (PSO) in FCM learning, thus producing a higher performance classification tool regarding the accuracy of the classification, and overcoming the limitations of FCMs in the pattern analysis area. © IFIP International Federation for Information Processing 2013