Multigrid Waveform Relaxation on Spatial Finite Element Meshes: The Discrete-Time Case

The efficiency of numerically solving time-dependent partial differential equations on parallel computers can be greatly improved by computing the solution on many time levels simultaneously. The theoretical properties of one such method, namely the discrete-time multigrid waveform relaxation method, are investigated for systems of ordinary differential equations obtained by spatial finite-element discretisation of linear parabolic initial-boundary value problems. The results are compared to the corresponding continuous-time results. The theory is illustrated for a one-dimensional and a two-dimensional model problem and checked against results obtained by numerical experiments

Shape based indexing for faster search of RNA family databases

Janssen S, Reeder J, Giegerich R. Shape based indexing for faster search of RNA family databases. BMC Bioinformatics. 2008;9(1):131.Background: Most non-coding RNA families exert their function by means of a conserved, common secondary structure. The Rfam data base contains more than five hundred structurally annotated RNA families. Unfortunately, searching for new family members using covariance models (CMs) is very time consuming. Filtering approaches that use the sequence conservation to reduce the number of CM searches, are fast, but it is unknown to which sacrifice. Results: We present a new filtering approach, which exploits the family specific secondary structure and significantly reduces the number of CM searches. The filter eliminates approximately 85% of the queries and discards only 2.6% true positives when evaluating Rfam against itself. First results also capture previously undetected non-coding RNAs in a recent human RNAz screen. Conclusion: The RNA shape index filter (RNAsifter) is based on the following rationale: An RNA family is characterised by structure, much more succinctly than by sequence content. Structures of individual family members, which naturally have different length and sequence composition, may exhibit structural variation in detail, but overall, they have a common shape in a more abstract sense. Given a fixed release of the Rfam data base, we can compute these abstract shapes for all families. This is called a shape index. If a query sequence belongs to a certain family, it must be able to fold into the family shape with reasonable free energy. Therefore, rather than matching the query against all families in the data base, we can first (and quickly) compute its feasible shape(s), and use the shape index to access only those families where a good match is possible due to a common shape with the query

Degradation of Chloroaromatics: Purification and Characterization of a Novel Type of Chlorocatechol 2,3-Dioxygenase of Pseudomonas putida GJ31

A purification procedure for a new kind of extradiol dioxygenase, termed chlorocatechol 2,3-dioxygenase, that converts 3-chlorocatechol productively was developed. Structural and kinetic properties of the enzyme, which is part of the degradative pathway used for growth of Pseudomonas putida GJ31 with chlorobenzene, were investigated. The enzyme has a subunit molecular mass of 33.4 kDa by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Estimation of the native Mr value under nondenaturating conditions by gel filtration gave a molecular mass of 135 ± 10 kDa, indicating a homotetrameric enzyme structure (4 × 33.4 kDa). The pI of the enzyme was estimated to be 7.1 ± 0.1. The N-terminal amino acid sequence (43 residues) of the enzyme was determined and exhibits 70 to 42% identity with other extradiol dioxygenases. Fe(II) seems to be a cofactor of the enzyme, as it is for other catechol 2,3-dioxygenases. In contrast to other extradiol dioxygenases, the enzyme exhibited great sensitivity to temperatures above 40°C. The reactivity of this enzyme toward various substituted catechols, especially 3-chlorocatechol, was different from that observed for other catechol 2,3-dioxygenases. Stoichiometric displacement of chloride occurred from 3-chlorocatechol, leading to the production of 2-hydroxymuconate.

Umformung von Magnesiumknetlegierungen auf Aluminium-Zink Basis im geschlossenen Gesenk

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Kisses, ambivalent models and more: Contributions to the analysis of RNA secondary structure.

Janssen S. Kisses, ambivalent models and more: Contributions to the analysis of RNA secondary structure. Bielefeld: Universitätsbibliothek; 2014.The full functional role of RNA in all domains of life is yet to be explored. Deep sequencing technologies generate massive data about RNA transcripts with functional potential. To decipher this information, bioinformatics methods for structural analysis are in demand. With this thesis at hand, we want to improve current secondary structure prediction in different respects. The introductory chapter explains ADP with a focus on its comfortable, but atypical style of specifying algorithms. Then, we present five contributions to the analysis of RNA secondary structures. 1. It is the nature of models to abstract and simplify reality in order to master its complexity. Chapter 3 is an in depth analysis of four popular computational models of RNA secondary structure (Programs RNAshapes and RNAalishapes). 2. The secondary structure of RNA is too dynamic to be described by a single structure and in turn, there is no single optimal secondary structure. Thus, we compute the most likely abstract shape of a given RNA sequence. Improvements of the algorithms for computing the likelihood of abstract shapes are discussed in Chapter 4, specifically with regards to computational speed (Program RapidShapes). 3. For computational complexity reasons, models of RNA structures commonly exclude crossing base-pairs, the so-called "pseudoknots", from the secondary structure. In Chapter 5, we introduce a heuristic for mastering a frequent type of pseudoknots: "kissing-hairpins" (Program pKiss). 4. In Chapter 6 we revisit the old algorithmic idea of outside-in computation for the new programming framework Bellman’s GAP. This broadens the arsenal of rapid prototyping algorithms for RNA and other sequential problems. It adds "outside" and "MEA" functionality to RNAshapes and RNAalishapes. 5. Covariance Models representing RNA families assume a single consensus secondary structure for a set of related RNAs and serve as statistical tools to search for additional members. In Chapter 7, we evaluate CM scorings that are more structurespecific than the standard sequence-to-model alignments. Furthermore, we introduce a technique to incorporate "ambivalent" consensus structures into covariance models (Program aCMs). The results of this work are available at the Bielefeld Bioinformatic Server. The RNA Studio (http://bibiserv.cebitec.uni-bielefeld.de/rna) supports ready to use web-submissions, web-services and cloud computing for the programs developed in this thesis. debian packages foster a simple way to install our software on your local machine. Developers can benefit from our algorithmic analyses or use our sources for rapid prototyping as a primer for new implementations: http://bibiserv.cebitec.uni-bielefeld.de/fold-grammars

Lost in folding space? Comparing four variants of the thermodynamic model for RNA secondary structure prediction

Janssen S, Schudoma C, Steger G, Giegerich R. Lost in folding space? Comparing four variants of the thermodynamic model for RNA secondary structure prediction. BMC Bioinformatics. 2011;12(1): 429.BACKGROUND:Many bioinformatics tools for RNA secondary structure analysis are based on a thermodynamic model of RNA folding. They predict a single, "optimal" structure by free energy minimization, they enumerate near-optimal structures, they compute base pair probabilities and dot plots, representative structures of different abstract shapes, or Boltzmann probabilities of structures and shapes. Although all programs refer to the same physical model, they implement it with considerable variation for different tasks, and little is known about the effects of heuristic assumptions and model simplifications used by the programs on the outcome of the analysis.RESULTS:We extract four different models of the thermodynamic folding space which underlie the programs RNAfold, RNAshapes, and RNAsubopt. Their differences lie within the details of the energy model and the granularity of the folding space. We implement probabilistic shape analysis for all models, and introduce the shape probability shift as a robust measure of model similarity. Using four data sets derived from experimentally solved structures, we provide a quantitative evaluation of the model differences.CONCLUSIONS:We find that search space granularity affects the computed shape probabilities less than the over- or underapproximation of free energy by a simplified energy model. Still, the approximations perform similar enough to implementations of the full model to justify their continued use in settings where computational constraints call for simpler algorithms. On the side, we observe that the rarely used level 2 shapes, which predict the complete arrangement of helices, multiloops, internal loops and bulges, include the "true" shape in a rather small number of predicted high probability shapes. This calls for an investigation of new strategies to extract high probability members from the (very large) level 2 shape space of an RNA sequence. We provide implementations of all four models, written in a declarative style that makes them easy to be modified. Based on our study, future work on thermodynamic RNA folding may make a choice of model based on our empirical data. It can take our implementations as a starting point for further program development

History Matters: How Short-Term Price Charts Hurt Investment Performance

When making investment decisions, people rely heavily on price charts displaying the past performance of an asset. Price charts can come with any time frame, which the provider might strategically choose. We analyze the impact of the time frame on retail investors’ behavior, particularly trading activity and risk-taking, in a controlled experiment with 1041 retail investors. We find that shorter time frames are associated with more trading activity, resulting in higher transaction fees and investor welfare losses. However, the time frame does not affect average risk-taking

Environmental toxicants in breast milk of Norwegian mothers and gut bacteria composition and metabolites in their infants at 1 month.

BACKGROUND:Early disruption of the microbial community may influence life-long health. Environmental toxicants can contaminate breast milk and the developing infant gut microbiome is directly exposed. We investigated whether environmental toxicants in breastmilk affect the composition and function of the infant gut microbiome at 1 month. We measured environmental toxicants in breastmilk, fecal short-chain fatty acids (SCFAs), and gut microbial composition from 16S rRNA gene amplicon sequencing using samples from 267 mother-child pairs in the Norwegian Microbiota Cohort (NoMIC). We tested 28 chemical exposures: polychlorinated biphenyls (PCBs), polybrominated flame retardants (PBDEs), per- and polyfluoroalkyl substances (PFASs), and organochlorine pesticides. We assessed chemical exposure and alpha diversity/SCFAs using elastic net regression modeling and generalized linear models, adjusting for confounders, and variation in beta diversity (UniFrac), taxa abundance (ANCOM), and predicted metagenomes (PiCRUSt) in low, medium, and high exposed groups. RESULTS:PBDE-28 and the surfactant perfluorooctanesulfonic acid (PFOS) were associated with less microbiome diversity. Some sub-OTUs of Lactobacillus, an important genus in early life, were lower in abundance in samples from infants with relative "high" (> 80th percentile) vs. "low" (< 20th percentile) toxicant exposure in this cohort. Moreover, breast milk toxicants were associated with microbiome functionality, explaining up to 34% of variance in acetic and propionic SCFAs, essential signaling molecules. Per one standard deviation of exposure, PBDE-28 was associated with less propionic acid (- 24% [95% CI - 35% to - 14%] relative to the mean), and PCB-209 with less acetic acid (- 15% [95% CI - 29% to - 0.4%]). Conversely, PFOA and dioxin-like PCB-167 were associated with 61% (95% CI 35% to 87%) and 22% (95% CI 8% to 35%) more propionic and acetic acid, respectively. CONCLUSIONS:Environmental toxicant exposure may influence infant gut microbial function during a critical developmental window. Future studies are needed to replicate these novel findings and investigate whether this has any impact on child health