To the Question of Rhododendron L. Genus Phylogeny Based on ITS1-ITS2 Spacers Sequence Studies

The first and second internal transcribed spacer (ITS1 and ITS2) regions of the ribosomal DNA and 5.8S rRNA gene from Rhododendron L. were analysed. This study reveals phylogenetic relationships and collation of data on the phylogeny of the genus Rhododendron L . according to the research of other authors using molecular and classical methods. Sequence analysis of ribosomal spacer showed low variability between species of the genus Rhododendron series of Dauricum. Rh. mucronulatum Turcz., Rh. dauricum L. and some other studied species had identical nucleotide ITS1-ITS2 sequence indicating the artificial division into separate species. Found species differing from each other by 1-2 or few nucleotides, which allows assuming their common phylogenetic affiliation or excluding one taxonomic unit. According to the analysis of ITS1-ITS2 sequences identified 16 groups of species with similar sequence ITS1-ITS2. When comparing the morphological descriptions of some species of the genus Rhododendron L. with a similar sequence of ITS1-ITS2 marked their small differences. Based on the results of molecular genetic analysis it has been assumed that Rhododendron dauricum L., Rh. ledebourii Pojark, Rh. sichotense Pojark and Rh. mucronulatum Turcz belong to the same species. The establishment phylogenetic relationships based on sequence ITS1-ITS2, applicable only in respect of highly isolated species Rhododendron L. To clarify the phylogenetic relationships of the genus Rhododendron L. necessary to expand the comparative analysis of the DNA sequences of universal genes or complex repeats elements (retrotransposons).Peer reviewe

Tailoring non-stoichiometry and mixed ionic-electronic conductivity in nanostructured Pr-substituted ceria

High concentrations of mobile oxygen vacancies are crucial for devices such as SOFCs, SOECs, gas permeation membranes, and sensors, while for other applications such as ferroelectrics and piezoelectrics, oxygen vacancies are detrimental. Hence there is great interest in tailoring the oxygen vacancy concentration and mobility for given materials. Changes in oxygen non-stoichiometry also result in dilation of the crystal lattice, known as chemical expansion, and therefore there is a coupling between the electrical, chemical, and mechanical properties known as electro-chemo-mechanical coupling. Confined systems, such as thin films, are being investigated as a way to tailor the non-stoichiometry and transport properties of materials, shifting the paradigm away from searching for new materials or compositions. Please click Additional Files below to see the full abstract

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

Optimizing a global alignment of protein interaction networks

Motivation: The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species’ evolution. Results: We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species. Availability: Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/.National Institutes of Health (U.S.) (Grant GM081871

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