Study on the process of Fe (III) oxide fluorination

The article deals with a fundamentally new fluoride technology for obtaining fluoride materials, provides data on the kinetics of the process of fluorination of Fe oxide with fluorine, fluoride and ammonium bifluoride. The physical and chemical properties of obtained fluorides are shown: a study of the elemental composition, grain-size composition using the method of scanning electron microscopy and laser diffraction

Study on the process of Fe (III) oxide fluorination

The article deals with a fundamentally new fluoride technology for obtaining fluoride materials, provides data on the kinetics of the process of fluorination of Fe oxide with fluorine, fluoride and ammonium bifluoride. The physical and chemical properties of obtained fluorides are shown: a study of the elemental composition, grain-size composition using the method of scanning electron microscopy and laser diffraction

AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo

A Novel Framework for the Comparative Analysis of Biological Networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes

Detection of gene orthology from gene co-expression and protein interaction networks

Background Ortholog detection methods present a powerful approach for finding genes that participate in similar biological processes across different organisms, extending our understanding of interactions between genes across different pathways, and understanding the evolution of gene families. Results We exploit features derived from the alignment of protein-protein interaction networks and gene-coexpression networks to reconstruct KEGG orthologs for Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository and Mus musculus and Homo sapiens and Sus scrofa gene coexpression networks extracted from NCBI\u27s Gene Expression Omnibus using the decision tree, Naive-Bayes and Support Vector Machine classification algorithms. Conclusions The performance of our classifiers in reconstructing KEGG orthologs is compared against a basic reciprocal BLAST hit approach. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit

Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks

Effect of internal radiation on the solid–liquid interface shape in low and high thermal gradient Czochralski oxide growth

International audienc

Impact of Oxygen Non‐Stoichiometry on Near‐Ambient Temperature Ionic Mobility in Polaronic Mixed‐Ionic‐Electronic Conducting Thin Films

Enhanced ionic mobility in mixed ionic and electronic conducting solids contributes to improved performance of memristive memory, energy storage and conversion, and catalytic devices. Ionic mobility can be significantly depressed at reduced temperatures, for example, due to defect association and therefore needs to be monitored. Measurements of ionic transport in mixed conductors, however, proves to be difficult due to dominant electronic conductivity. This study examines the impact of different levels of quenched-in oxygen deficiency on the oxygen vacancy mobility near room temperature. A praseodymium doped ceria (Pr0.1Ce0.9O2–δ ) film is grown by pulsed laser deposition and annealed in various oxygen partial pressures to modify its oxygen vacancy concentration. Changes in film non-stoichiometry are monitored by tracking the optical absorption related to the oxidation state of Pr ions. A 13-fold increase in ionic mobility at 60 °C for increases in oxygen non-stoichiometry from 0.032 to 0.042 is detected with negligible changes in migration enthalpy and large changes in pre-factor. Several factors potentially contributing to the large pre-factor changes are examined and discussed. Insights into how ionic defect concentration can markedly impact ionic mobility should help in elucidating the origins of variations seen in nanoionic devices