Epitaxial Bi2FeCrO6 Multiferroic Thin Films

We present here experimental results obtained on Bi2FeCrO6 (BFCO) epitaxial films deposited by laser ablation directly on SrTiO3 substrates. It has been theoretically predicted, by Baettig and Spaldin, using first-principles density functional theory that BFCO is ferrimagnetic (with a magnetic moment of 2 Bohr magneton per formula unit) and ferroelectric (with a polarization of ~80 microC/cm2 at 0K). The crystal structure has been investigated using X-ray diffraction which shows that the films are epitaxial with a high crystallinity and have a degree of orientation depending of the deposition conditions and that is determined by the substrate crystal structure. Chemical analysis carried out by X-ray Microanalysis and X-ray Photoelectron Spectroscopy (XPS) indicates the correct cationic stoichiometry in the BFCO layer, namely (Bi:Fe:Cr = 2:1:1). XPS depth profiling revealed that the oxidation state of Fe and Cr ions in the film remains 3+ throughout the film thickness and that both Fe and Cr ions are homogeneously distributed throughout the depth. Cross-section high-resolution transmission electron microscopy images together with selected area electron diffraction confirm the crystalline quality of the epitaxial BFCO films with no identifiable foreign phase or inclusion. The multiferroic character of BFCO is proven by ferroelectric and magnetic measurements showing that the films exhibit ferroelectric and magnetic hysteresis at room temperature. In addition, local piezoelectric measurements carried out using piezoresponse force microscopy (PFM) show the presence of ferroelectric domains and their switching at the sub-micron scale.Comment: Accepted for publication in Philosophical Magazine Letter

Modularization of biochemical networks based on classification of Petri net t-invariants

<p>Abstract</p> <p>Background</p> <p>Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.</p> <p>With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system.</p> <p>Methods</p> <p>Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied.</p> <p>Results</p> <p>We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in <it>Saccharomyces cerevisiae</it>) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability.</p> <p>Conclusion</p> <p>We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.</p

Psychometric Properties of the Parent and Teacher Versions of the Strengths and Difficulties Questionnaire for 4- to 12-Year-Olds: A Review

Since its development, the Strengths and Difficulties Questionnaire (SDQ) has been widely used in both research and practice. The SDQ screens for positive and negative psychological attributes. This review aims to provide an overview of the psychometric properties of the SDQ for 4- to 12-year-olds. Results from 48 studies (N = 131,223) on reliability and validity of the parent and teacher SDQ are summarized quantitatively and descriptively. Internal consistency, test–retest reliability, and inter-rater agreement are satisfactory for the parent and teacher versions. At subscale level, the reliability of the teacher version seemed stronger compared to that of the parent version. Concerning validity, 15 out of 18 studies confirmed the five-factor structure. Correlations with other measures of psychopathology as well as the screening ability of the SDQ are sufficient. This review shows that the psychometric properties of the SDQ are strong, particularly for the teacher version. For practice, this implies that the use of the SDQ as a screening instrument should be continued. Longitudinal research studies should investigate predictive validity. For both practice and research, we emphasize the use of a multi-informant approach