Health-related quality of life, adiposity, and sedentary behavior in patients with early schizophrenia: Preliminary study

Objective: To examine adiposity and sedentary behavior in relation to health-related quality of life (QoL) in patients with early schizophrenia. Methods: A cross-sectional study was used to assess adiposity by dual-energy X-ray absorptiometry scans, habitual physical activity and idle sitting time by the Short Form International Physical Activity Questionnaire, and health-related QoL by the RAND Medical Outcomes Study SF-36. QoL scores were compared with age-adjusted Canadian normative population data. Results: There were 36 participants with early schizophrenia, average age 25.1 (±3.6). Twenty-nine (72.5%) were males. Mean illness duration was 30 (±18) months, and mean body mass index was 28.3 (±5). Females had higher body fat content than males (30.8±6.9 vs 24.7±10.6; t=-2.6, df=34; P=0.015). Total body fat (F=14; P=0.001), lean body mass (F=10.2; P=0.001), and sedentary behavior (F=5; P=0.013) significantly increased across body mass index categories. Total body fat was correlated with sedentary behavior (r=0.62; P=0.001), and total lean body mass was negatively correlated with sedentary behavior (r=0.39; P=0.03). Based on SF-36scores, participants had significantly lower physical functioning (P=0.0034), role physical (P=0.0003), general health (P,0.0001), vitality (P=0.03), and physical component scores (P=0.003) than Canadian population comparisons. Habitual sedentary behavior, more than activity or adiposity levels, was associated with health-related QoL in early schizophrenia. Conclusion: Health-related QoL is lower in early schizophrenia and is predominantly experienced in the physical domain. QoL in early schizophrenia relates to sedentary behavior more than to activity and adiposity levels. © 2012 Strassnig etal, publisher and licensee Dove Medical Press Ltd

A mathematical theory of semantic development in deep neural networks

An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge, thereby raising a fundamental conceptual question: what are the theoretical principles governing the ability of neural networks to acquire, organize, and deploy abstract knowledge by integrating across many individual experiences? We address this question by mathematically analyzing the nonlinear dynamics of learning in deep linear networks. We find exact solutions to this learning dynamics that yield a conceptual explanation for the prevalence of many disparate phenomena in semantic cognition, including the hierarchical differentiation of concepts through rapid developmental transitions, the ubiquity of semantic illusions between such transitions, the emergence of item typicality and category coherence as factors controlling the speed of semantic processing, changing patterns of inductive projection over development, and the conservation of semantic similarity in neural representations across species. Thus, surprisingly, our simple neural model qualitatively recapitulates many diverse regularities underlying semantic development, while providing analytic insight into how the statistical structure of an environment can interact with nonlinear deep learning dynamics to give rise to these regularities

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case of deep linear neural networks. Despite the linearity of their input-output map, such networks have nonlinear gradient descent dynamics on weights that change with the addition of each new hidden layer. We show that deep linear networks exhibit nonlinear learning phenomena similar to those seen in simulations of nonlinear networks, including long plateaus followed by rapid transitions to lower error solutions, and faster convergence from greedy unsupervised pretraining initial conditions than from random initial conditions. We provide an analytical description of these phenomena by finding new exact solutions to the nonlinear dynamics of deep learning. Our theoretical analysis also reveals the surprising finding that as the depth of a network approaches infinity, learning speed can nevertheless remain finite: for a special class of initial conditions on the weights, very deep networks incur only a finite, depth independent, delay in learning speed relative to shallow networks. We show that, under certain conditions on the training data, unsupervised pretraining can find this special class of initial conditions, while scaled random Gaussian initializations cannot. We further exhibit a new class of random orthogonal initial conditions on weights that, like unsupervised pre-training, enjoys depth independent learning times. We further show that these initial conditions also lead to faithful propagation of gradients even in deep nonlinear networks, as long as they operate in a special regime known as the edge of chaos.Comment: Submission to ICLR2014. Revised based on reviewer feedbac

Phase analysis and dielectric properties of ceramics in PbO-MgO-ZnO-Nb<SUB>2</SUB>O<SUB>5</SUB> system: a comparative study of materials obtained by ceramic and molten salt synthesis routes

Compositions of the type 3PbO-MgO/ZnO-Nb2O5 were synthesized by the ceramic route at 1000&#176;C and sintered at 1200&#176;C. Powder X-ray diffraction studies of the 1000&#176;C heated products show the presence of the cubic pyrochlore and the columbite (Mg/ZnNb2O6) type phase in the ratio of 3 : 1 for all possible combinations of MgO and ZnO. Further heating at 1200&#176;C led to a decrease in the cubic pyrochlore phase and an increase in the columbite phase by around 10%. Compacted pellets sintered further showed the appearance of the perovskite phase. Similar compositions synthesized using the KCl-NaCl molten salt method at 900&#176;C for 6 h gave a significant amount of the cubic perovskite related phase of the type Pb(Mg/Zn)1/3Nb2/3O3 for all compositions containing MgO. The amount of the perovskite phase was nearly 55% for the Mg rich compositions and decreased with increase in Zn content, the pure Zn composition yielding mainly the cubic pyrochlore phase. On sintering these phases at 1000&#176;C the perovskite phase content decreased. The dielectric constant of the composite materials formed by the ceramic route was in the region of 14 to 20 and varied little with frequency. The composites obtained by the molten salt method, however, showed much larger dielectric constants in the region 40-150 at 500 kHz for various compositions. The dielectric loss tangent of these composites were lower by an order (0.005-0.03 at 500 kHz) compared to the ceramic route

Self-assembling behaviour of Pt nanoparticles onto surface of TiO2 and their resulting photocatalytic activity

In the present study, self-assembling behaviour of guest nanoparticles (platinum) onto the surface of host support (titanium dioxide) during photodeposition process as a function of solution pH has been explored in detail by means of transmission electron microscope (TEM). The photocatalytic activity of the resulting bimetallic nanoassembly (Pt/TiO2) was evaluated by studying the degradation of two organic pollutants viz. triclopyr and methyl orange. Microscopic studies revealed that the deposition and/or distribution of Pt nanoparticles onto the surface of TiO2 were strongly guided by the ionization state of support which in turn was regulated by the solution pH of photodeposition process. A direct relationship between the solution pH of deposition process and the photocatalytic activity of resulting bimetallic catalyst has been observed. A mechanism based on the interparticle interaction between TiO2 and hydrolytic products of metal ions has been proposed for the differences in the photocatalytic activity of the resulting nanocomposite

Influence of strontium on the cubic to ordered hexagonal phase transformation in barium magnesium niobate

Oxides of the type Ba3−xSrxMgNb2O9 were synthesized by the solid state route. The x=0 composition (Ba3MgNb2O9) was found to crystallize in a disordered (cubic) perovskite structure when sintered at 1000C. For higher Sr doping (x ≥ 0.5), there was clearly the presence of an ordered hexagonal phase indicated by the growth of superstructure reflections in the powder X-ray diffraction patterns. In all the compositions there was the presence of a minor amount of Ba5−xSrxNb4O15 phase which increased with Sr substitution up to x =1 and then it remained nearly constant at about 5%. Samples sintered at 1300C showed the hexagonally ordered phase for the entire range of composition (0 ≤x ≤ 3). The degree of ordering being considerably greater than in the 1000C heated samples as evidenced by several superstructure reflections

Synthesis of Ba<SUB>3</SUB>ZnNb<SUB>2</SUB>O<SUB>9</SUB>-Sr<SUB>3</SUB>ZnNb<SUB>2</SUB>O<SUB>9</SUB> solid solution and their dielectric properties

Oxides of the type, Ba3−xSrxZnNb2O9 (0 ≤ x ≤ 3), were synthesized by the solid state route. Oxides calcined at 1000°C show single cubic phase for all the compositions. The cubic lattice parameter (a) decreases with increase in Sr concentration from 4·0938(2) forx = 0 to 4·0067(2) for x = 3. Scanning electron micrographs show maximum grain size for thex = 1 composition (~ 2 μm) at 1200°C. Disks sintered at 1200°C show dielectric constant variation between 28 and 40 (at 500 kHz) for different values of x with the maximum dielectric constant at x = 1

Probabilistic Characterization of Hydrologic Extremes Using Bivariate Copulas

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv