Explaining the lack of dynamics in the diffusion of small stationary fuel cells

Using the reaction of hydrogen with oxygen to water in order to produce electricity and heat, promises a high electrical efficiency even in small devices which can be installed close to the consumer. This approach seems to be an impressive idea to contribute to a viable future energy supply under the restrictions of climate change policy. Major reasons currently hampering the diffusion of such technologies for house energy supply in Germany are analysed in this paper. The barriers revealed, include high production costs as well as economic and legal obstacles for installing the devices so that they can be operated in competition to central power plants, beside others in tenancies.fuel cell, diffusion processes, valuation of environmental effects, technological innovation

Strahlendefekte in Siliziumkarbid : Untersuchungen mit optisch detektierter Elektronen-paramagnetischer Resonanz

von Thomas LingnerPaderborn, Univ., Diss., 200

Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences

With the advent of metatranscriptomics it has now become possible to study the dynamics of microbial communities. The analysis of environmental RNA-Seq data implies several challenges for the development of efficient tools in bioinformatics. One of the first steps in the computational analysis of metatranscriptomic sequencing reads requires the separation of rRNA and mRNA fragments to ensure that only protein coding sequences are actually used in a subsequent functional analysis. In the context of the rRNA filtering task it is desirable to have a broad spectrum of different methods in order to find a suitable trade-off between speed and accuracy for a particular dataset. We introduce a machine learning approach for the detection of rRNA in metatranscriptomic sequencing reads that is based on support vector machines in combination with dinucleotide distance histograms for feature representation. The results show that our SVM-based approach is at least one order of magnitude faster than any of the existing tools with only a slight degradation of the detection performance when compared to state-of-the-art alignment-based methods

MarVis: a tool for clustering and visualization of metabolic biomarkers

<p>Abstract</p> <p>Background</p> <p>A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.</p> <p>Results</p> <p>We present the tool MarVis (Marker Visualization) for data mining on intensity-based profiles using one-dimensional self-organizing maps (1D-SOMs). MarVis can import and export customizable CSV (Comma Separated Values) files and provides aggregation and normalization routines for preprocessing of intensity profiles that contain repeated measurements for a number of different experimental conditions. Robust clustering is then achieved by training of an 1D-SOM model, which introduces a similarity-based ordering of the intensity profiles. The ordering allows a convenient visualization of the intensity variations within the data and facilitates an interactive aggregation of clusters into larger blocks. The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data.</p> <p>Conclusion</p> <p>MarVis is a user-friendly and interactive tool for exploration of complex pattern variation in a large set of experimental intensity profiles. The application of 1D-SOMs gives a convenient overview on relevant profiles and groups of profiles. The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown. Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.</p

RAD51-dependent recruitment of TERRA lncRNA to telomeres through R-loops

Telomeres- repeated, noncoding nucleotide motifs and associated proteins that are found at the ends of eukaryotic chromosomes—mediate genome stability and determine cellular lifespan. Telomeric-repeat-containing RNA (TERRA) is a class of long noncoding RNAs (lncRNAs) that are transcribed from chromosome ends; these RNAs in turn regulate telomeric chromatin structure and telomere maintenance through the telomere-extending enzyme telomerase and homology-directed DNA repair. The mechanisms by which TERRA is recruited to chromosome ends remain poorly defined. Here we develop a reporter system with which to dissect the underlying mechanisms, and show that the UUAGGG repeats of TERRA are both necessary and sufficient to target TERRA to chromosome ends. TERRA preferentially associates with short telomeres through the formation of telomeric DNA–RNA hybrid (R-loop) structures that can form in trans. Telomere association and R-loop formation trigger telomere fragility and are promoted by the recombinase RAD51 and its interacting partner BRCA2, but counteracted by the RNA-surveillance factors RNaseH1 and TRF1. RAD51 physically interacts with TERRA and catalyses R-loop formation with TERRA in vitro, suggesting a direct involvement of this DNA recombinase in the recruitment of TERRA by strand invasion. Together, our findings reveal a RAD51-dependent pathway that governs TERRA-mediated R-loop formation after transcription, providing a mechanism for the recruitment of lncRNAs to new loci in trans

Predicting phenotypic traits of prokaryotes from protein domain frequencies

BACKGROUND: Establishing the relationship between an organism's genome sequence and its phenotype is a fundamental challenge that remains largely unsolved. Accurately predicting microbial phenotypes solely based on genomic features will allow us to infer relevant phenotypic characteristics when the availability of a genome sequence precedes experimental characterization, a scenario that is favored by the advent of novel high-throughput and single cell sequencing techniques. RESULTS: We present a novel approach to predict the phenotype of prokaryotes directly from their protein domain frequencies. Our discriminative machine learning approach provides high prediction accuracy of relevant phenotypes such as motility, oxygen requirement or spore formation. Moreover, the set of discriminative domains provides biological insight into the underlying phenotype-genotype relationship and enables deriving hypotheses on the possible functions of uncharacterized domains. CONCLUSIONS: Fast and accurate prediction of microbial phenotypes based on genomic protein domain content is feasible and has the potential to provide novel biological insights. First results of a systematic check for annotation errors indicate that our approach may also be applied to semi-automatic correction and completion of the existing phenotype annotation

CoMet—a web server for comparative functional profiling of metagenomes

Analyzing the functional potential of newly sequenced genomes and metagenomes has become a common task in biomedical and biological research. With the advent of high-throughput sequencing technologies comparative metagenomics opens the way to elucidate the genetically determined similarities and differences of complex microbial communities. We developed the web server ‘CoMet’ (http://comet.gobics.de), which provides an easy-to-use comparative metagenomics platform that is well-suitable for the analysis of large collections of metagenomic short read data. CoMet combines the ORF finding and subsequent assignment of protein sequences to Pfam domain families with a comparative statistical analysis. Besides comprehensive tabular data files, the CoMet server also provides visually interpretable output in terms of hierarchical clustering and multi-dimensional scaling plots and thus allows a quick overview of a given set of metagenomic samples

Word correlation matrices for protein sequence analysis and remote homology detection

<p>Abstract</p> <p>Background</p> <p>Classification of protein sequences is a central problem in computational biology. Currently, among computational methods discriminative kernel-based approaches provide the most accurate results. However, kernel-based methods often lack an interpretable model for analysis of discriminative sequence features, and predictions on new sequences usually are computationally expensive.</p> <p>Results</p> <p>In this work we present a novel kernel for protein sequences based on average word similarity between two sequences. We show that this kernel gives rise to a feature space that allows analysis of discriminative features and fast classification of new sequences. We demonstrate the performance of our approach on a widely-used benchmark setup for protein remote homology detection.</p> <p>Conclusion</p> <p>Our word correlation approach provides highly competitive performance as compared with state-of-the-art methods for protein remote homology detection. The learned model is interpretable in terms of biologically meaningful features. In particular, analysis of discriminative words allows the identification of characteristic regions in biological sequences. Because of its high computational efficiency, our method can be applied to ranking of potential homologs in large databases.</p