A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Abstract Background Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges. Results The results shown here are for an adapted subset of aSaccharomyces cerevisiaegene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions. Conclusion Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses

Non-L\'evy mobility patterns of Mexican Me'Phaa peasants searching for fuelwood

We measured mobility patterns that describe walking trajectories of individual Me'Phaa peasants searching and collecting fuelwood in the forests of "La Monta\~na de Guerrero" in Mexico. These one-day excursions typically follow a mixed pattern of nearly-constant steps when individuals displace from their homes towards potential collecting sites and a mixed pattern of steps of different lengths when actually searching for fallen wood in the forest. Displacements in the searching phase seem not to be compatible with L\'evy flights described by power-laws with optimal scaling exponents. These findings however can be interpreted in the light of deterministic searching on heavily degraded landscapes where the interaction of the individuals with their scarce environment produces alternative searching strategies than the expected L\'evy flights. These results have important implications for future management and restoration of degraded forests and the improvement of the ecological services they may provide to their inhabitants.Comment: 15 pages, 4 figures. First version submitted to Human Ecology. The final publication will be available at http://www.springerlink.co

Efeito da adição de coating de cromita de ferro na emissividade de concreto refratário de alta alumina

Resumo Coatings de alta emissividade têm sido utilizados em aplicações industriais há mais de 40 anos com o objetivo de reduzir as perdas térmicas em processos de aquecimento. Com a aplicação de um coating de alta emissividade na superfície dos revestimentos internos de um forno industrial é possível aumentar a eficiência nas trocas térmicas entre a atmosfera e as paredes do revestimento, reduzindo a perda de calor e também o consumo de combustíveis. Em geral são utilizados compostos cerâmicos como óxido de cério, carbeto de boro, boreto de silício, siliceto de molibidênio ou óxido de cromo como agentes de emissividade para se obter as propriedades termo-ópticas desejadas nos coatings. Entretanto tais compostos muitas vezes inviabilizam sua aplicação, devido ao seu elevado valor comercial ou ainda sua escassez. Neste contexto, o presente trabalho buscou avaliar os efeitos da cromita de ferro, um óxido mineral abundante e de valor comercial acessível, em sua utilização como agente de emissividade em coatings refratários de alta emissividade. Por meio de um método indireto de medição de emissividade, foram avaliadas de maneira comparativa composições com e sem a presença da cromita de ferro visando sua aplicação como cobertura de revestimentos isolantes e refratários de fornos de aquecimento industrial. Com adições de 7,5% de cromita de ferro, observaram-se aumentos na emissividade dos coatings da ordem de 8%, sugerindo seu potencial para essa aplicação

A methodology to infer gene networks from spatial patterns of expression: an application to fluorescence in situ hybridization images

The proper functional development of a multicellular organism depends on an intricate network of interacting genes that are expressed in accurate temporal and spatial patterns across different tissues. Complex inhibitory and excitatory interactions among genes control the territorial differences that explain specialized cell fates, embryo polarization and tissues architecture in metazoans. Given the nature of the regulatory gene networks, similarity of expression patterns can identify genes with similar roles. The inference and analysis of the gene interaction networks through complex network tools can reveal important aspects of the biological system modeled. Here we suggest an image analysis pipeline to quantify co-localization patterns in in situ hybridization images of Drosophila embryos and, based on these patterns, infer gene networks. We analyze the spatial dispersion of the gene expression and show the gene interaction networks for different developmental stages. Our results suggest that the inference of developmental networks based on spatial expression data is biologically relevant and represents a potential tool for the understanding of animal development.FAPESP (99/12765-2, 05/00587-5, 09/16799-2, 11/22639-8)CNPq (301303/06-1)Human Frontier Science Program (RGP39/2002)Medical Research Council (U105185859

A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Abstract Background Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges. Results The results shown here are for an adapted subset of a Saccharomyces cerevisiae gene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions. Conclusion Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses.</p