Enhancement of bubble transport in porous electrodes and catalysts.

We investigate the formation and transport of gas bubbles across a model porous electrode/catalyst using lattice Boltzmann simulations. This approach enables us to systematically examine the influence of a wide range of morphologies, flow velocities, and reaction rates on the efficiency of gas production. By exploring these parameters, we identify critical parameter combinations that significantly contribute to an enhanced yield of gas output. Our simulations reveal the existence of an optimal pore geometry for which the product output is maximized. Intriguingly, we also observe that lower flow velocities improve gas production by leveraging coalescence-induced bubble detachment from the electrode/catalyst.

3-Hydroxy-3-methylglutaryl coenzyme A reductase inhibitor Atorvastatin mediated effects depend on the activation status of target cells in PLP-EAE.

The effect of Atorvastatin on transcriptional activity in murine experimental autoimmune encephalomyelitis (EAE) induced by PLP peptide 139-151 was analyzed by DNA microarray technique in lymph nodes and spinal cord at onset (10 days), height (20 days) and first remission (30 days) of disease. Fourteen genes were selectively influenced by Atorvastatin in EAE mice. They are mainly related to immune cell functions and regulation of cell-to-cell interaction. Interestingly, seven genes were also differentially regulated in CFA-injected control mice. But qualitative and quantitative differences to EAE mice argue for a dependency of statin effects on the activation status of target cells. Differential regulation of the newly detected candidate genes of statin effects COX-1 and HSP-105 and the previously known statin-responsive genes ICAM-1 and CD86 was confirmed by Western blot and immunohistochemistry. Flow cytometric analysis of lymph node cells revealed that the effect of Atorvastatin treatment in non-immunized healthy animals resembled the effect of immunization with PLP peptide regarding changes of T helper cells, activated B cells and macrophages. In EAE mice, these effects were partially reversed by Atorvastatin treatment. Monitoring of expression of the newly identified candidate genes and patterns of lymphocyte subpopulations might predict the responsiveness of multiple sclerosis patients to statin treatment.

Representation of individual gene expression in completely pooled mRNA samples.

Designing microarray experiments, scientists are often confronted with the question of pooling due to financial constraints, but discussion of the validity of pooling tends toward a sub-pooling recommendation. Since complete pooling protocols can be considered part of sub-pooling designs, gene expression data from three complete pooling experiments were analyzed. Data from complete pooled versus individual mRNA samples of rat brain tissue were compared to answer the question whether the pooled sample represents individual samples in small-sized experiments. Our analytic approach provided clear results concerning the Affymetrix MAS 5.0 signal and detection call parameters. Despite a strong similarity of arrays within experimental groups, the individual signals were evidently not appropriately represented in the pooled sample, with slightly more than half of all the genes considered. Our analysis reveals problems in cases of small complete pooling designs with less than six subjects pooled.

Automatic construction of gene relation networks using text mining and gene expression data.

Microarray gene expression analysis is a powerful high-throughput technique that enables researchers to monitor the expression of thousands of genes simultaneously. Using this methodology huge amounts of data are produced which have to be analysed. Clustering algorithms are used to group genes together based on a predefined distance measure. However, clustering algorithms do not necessarily group the genes in a biological meaningful way. Additional information is needed to improve the identification of disease relevant genes. The primary objective of our project is to support the analysis of microarray gene expression data by construction of gene relation networks (GRNs). Required information can not be found in a structured representation like a database. In contrast, a large number of relations are described in biomedical literature. The main outcome of this project is the implementation of a software system that provides clinicians and researchers with a tool that supports the analysis of microarray gene expression data by mapping known relationships from the biomedical literature to local gene expression experiments.