Implementation of a Hebbian chemoreceptor model for diffusive source localization

While new approaches to chemical localization have been proposed, animals are still widely used for locating landmines and illegal substances. Existing electronic noses still do not have the necessary sensitivity and accuracy. By modeling a cell’s chemical detection system, we can gain insight into the basic “olfactory” system. We use an inspiration from chemotaxis and Hebbian learning to enhance localization and tracking of gradient sources, which can be applied to both chemicals and heat. The eukaryotic receptor clustering model shows improvement over previous prokaryotic chemotaxis-inspired methods that do not take into account receptor clustering. Receptor clustering essentially adapts receptors spatio-temporally. For a mobile simulation, our method locates the source in less convergence time than the other chemotaxis algorithms and insignificantly less time compared to no spatio-temporal filtering (e.g. a single-sensor memoryless case). We then show that local regions of receptor cooperation have the best performance reflecting observations of receptor behavior in biology. To demonstrate the performance of this system in real-time, a stationary 4/8-sensor version of the array is implemented, and the algorithm improves the convergence time, mean, and variance of the Direction-of-Arrival calculation in diffusive, turbulent, and noisy environments.     

Pichia stipitis genomics, transcriptomics, and gene clusters

Genome sequencing and subsequent global gene expression studies have advanced our understanding of the lignocellulose-fermenting yeast Pichia stipitis . These studies have provided an insight into its central carbon metabolism, and analysis of its genome has revealed numerous functional gene clusters and tandem repeats. Specialized physiological traits are often the result of several gene products acting together. When coinheritance is necessary for the overall physiological function, recombination and selection favor colocation of these genes in a cluster. These are particularly evident in strongly conserved and idiomatic traits. In some cases, the functional clusters consist of multiple gene families. Phylogenetic analyses of the members in each family show that once formed, functional clusters undergo duplication and differentiation. Genome-wide expression analysis reveals that regulatory patterns of clusters are similar after they have duplicated and that the expression profiles evolve along with functional differentiation of the clusters. Orthologous gene families appear to arise through tandem gene duplication, followed by differentiation in the regulatory and coding regions of the gene. Genome-wide expression analysis combined with cross-species comparisons of functional gene clusters should reveal many more aspects of eukaryotic physiology.     

Mining functional information associated with expression arrays

Deciphering the networks of interactions between molecules in biological systems has gained momentum with the monitoring of gene expression patterns at the genomic scale. Expression array experiments provide vast amounts of experimental data about these networks, the analysis of which requires new computational methods. In particular, issues related to the extraction of biological information are key for the end users. We propose here a strategy, implemented in a system called GEISHA (gene expression information system for human analysis) and able to detect biological terms significantly associated to different gene expression clusters by mining collections of Medline abstracts. GEISHA is based on a comparison of the frequency of abstracts linked to different gene clusters and containing a given term. Interpretation by the end user of the biological meaning of the terms is facilitated by embedding them in the corresponding significant sentences and abstracts and by establishing relations with other, equally significant terms. The information provided by GEISHA for the available yeast expression data compares favorably with the functional annotations provided by human experts, demonstrating the potential value of GEISHA as an assistant for the analysis of expression array experiments. Electronic Publication     

Improvements in the determination of radionuclide distribution in the analysis of irradiated samples using double-differentiation method

The spatial response of an 8x4 block detector made up of 5.6-mm-wide, 12.9-mm-high, 30-mm-thick individual detector crystals to a collimated line source of 511 keV annihilation photons was examined. The response of each crystal showed a spread around the average positioning values and distributions from adjacent crystals overlapped as the collimated source scanned the individual detectors. This leads to possible errors in the event assignment. The implementation of double differentiation or the second derivative method was proposed for the removal of scattered photons so as to reduce the overlap and, hence, avoid mispositioning. This method is a mathematical solution implemented when analysing the results. A curve in a spatial spectrum could be considered to be a function f(x), wherex is the position. When double differentiation of f(x) is carried out, then the normalized curve d²f(x) appears with some reduction in the wings. It was shown that a reduction of the scattering contribution in the tails without overestimating the contribution of scattered events could be achieved by implementing a double-differentiation process.     

Proteomic analysis of tissue samples in translational breast cancer research

In the last decade, many proteomic technologies have been applied, with varying success, to the study of tissue samples of breast carcinoma for protein expression profiling in order to discover protein biomarkers/signatures suitable for: characterization and subtyping of tumors; early diagnosis, and both prognosis and prediction of outcome of chemotherapy. The purpose of this review is to critically appraise what has been achieved to date using proteomic technologies and to bring forward novel strategies – based on the analysis of clinically relevant samples – that promise to accelerate the translation of basic discoveries into the daily breast cancer clinical practice. In particular, we address major issues in experimental design by reviewing the strengths and weaknesses of current proteomic strategies in the context of the analysis of human breast tissue specimens.     

Quantitative and rapid analysis of transglutaminase activity using protein arrays in mammalian cells

We developed a novel on-chip activity assay using protein arrays for quantitative and rapid analysis of transglutami-nase activity in mammalian cells. Transglutaminases are a family of Ca²⁺-dependent enzymes involved in cell regulation as well as human diseases such as neurodegenerative disorders, inflammatory diseases and tumor progression. We fabricated the protein arrays by immobilizing N,N′-dimethylcasein (a substrate) on the amine surface of the arrays. We initiated transamidating reaction on the protein arrays and determined the transglutaminase activity by analyzing the fluorescence intensity of biotinylated casein. The on-chip transglutaminase activity assay was proved to be much more sensitive than the [³H]putrescine-incorporation assay. We successfully applied the on-chip assay to a rapid and quantitative analysis of the transgluta-minase activity in all-trans retinoic acid-treated NIH 3T3 and SH-SY5Y cells. In addition, the on-chip transglutaminase activity assay was sufficiently sensitive to determine the transglutaminase activity in eleven mammalian cell lines. Thus, this novel on-chip transglutaminase activity assay was confirmed to be a sensitive and high-throughput approach to investigating the roles of transglutaminase in cellular signaling, and, moreover, it is likely to have a strong potential for monitoring human diseases. These authors contributed equally to this work.