Carbono orgânico e atributos microbiológicos em solo agrícola com diferentes níveis de produtividade de soja em sistema de plantio direto.

Atributos microbiológicos e bioquímicos do solo são geralmente negligenciados ao se relacionar a fertilidade do solo e a produtividade das culturas, em que o carbono orgânico (C) apresenta papel chave. O objetivo deste trabalho foi avaliar alguns bioindicadores de qualidade de solo e relacioná-los com o teor de C do solo em áreas de produção comercial com diferentes níveis de produtividade de soja sob sistema de plantio direto. Amostras compostas de solo foram obtidas na profundidade 0-10 cm, em 6 áreas agrícolas em Ponta Grossa, PR. Os dados foram submetidos à análise de correlação de Pearson e regressão múltipla. A regressão múltipla mostrou que a produtividade é influenciada principalmente pelo teor de C orgânico no solo, e a correlação simples de Pearson mostrou-se significativa entre o C orgânico e a biomassa microbiana de carbono (BMC), biomassa microbiana de nitrogênio (BMN), capacidade de troca de cátions (CTC) e atividades enzimáticas. Conclui-se que há uma relação entre a produtividade de grãos de soja e a atividade microbiana no solo, em que o C orgânico apresenta papel chave.Fertbio

Response network analysis of differential gene expression in human epithelial lung cells during avian influenza infections

<p>Abstract</p> <p>Background</p> <p>The recent emergence of the H5N1 influenza virus from avian reservoirs has raised concern about future influenza strains of high virulence emerging that could easily infect humans. We analyzed differential gene expression of lung epithelial cells to compare the response to H5N1 infection with a more benign infection with Respiratory Syncytial Virus (RSV). These gene expression data are then used as seeds to find important nodes by using a novel combination of the Gene Ontology database and the Human Network of gene interactions. Additional analysis of the data is conducted by training support vector machines (SVM) with the data and examining the orientations of the optimal hyperplanes generated.</p> <p>Results</p> <p>Analysis of gene clustering in the Gene Ontology shows no significant clustering of genes unique to H5N1 response at 8 hours post infection. At 24 hours post infection, however, a number of significant gene clusters are found for nodes representing "immune response" and "response to virus" terms. There were no significant clusters of genes in the Gene Ontology for the control (Mock) or RSV experiments that were unique relative to the H5N1 response. The genes found to be most important in distinguishing H5N1 infected cells from the controls using SVM showed a large degree of overlap with the list of significantly regulated genes. However, though none of these genes were members of the GO clusters found to be significant.</p> <p>Conclusions</p> <p>Characteristics of H5N1 infection compared to RSV infection show several immune response factors that are specific for each of these infections. These include faster timescales within the cell as well as a more focused activation of immunity factors. Many of the genes that are found to be significantly expressed in H5N1 response relative to the control experiments are not found to cluster significantly in the Gene Ontology. These genes are, however, often closely linked to the clustered genes through the Human Network. This may suggest the need for more diverse annotations of these genes and verification of their action in immune response.</p

Domain ontology for digital marketplaces

Recently the sharing economy has emerged as a viable alternative to fulfilling a variety of consumer needs. As there is no consensus on the definition of ‘sharing economy’ we use the term ‘marketplace’ to refer more specifically to Internet/software-based sharing economy platforms connecting two different market segments. In the field of sharing economy and marketplaces we found a research gap concerning the (socio)technological aspects and the development of marketplaces. A marketplace ontology can help to have a clear account of marketplace concepts which will facilitate communication, consensus and alignment. In this paper we design this marketplace ontology in four steps. First the selection of UFO as foundation and UFO-S as core ontology. Second the search for a set of minimal conditions and properties common for marketplaces and the derivation into competency questions. Third, use the competency questions to identify fragmented sub-ontology pieces called Domain-Related Ontology Patterns (DROPs) and apply them informally by extending UFO-S concepts to design a marketplace domain ontology. This marketplace domain ontology is represented in OntoUML. The last step is the validation of the OntoUML model using expert knowledge

The tight groupoid of an inverse semigroup

In this work we present algebraic conditions on an inverse semigroup S (with zero) which imply that its associated tight groupoid G_tight(S) is: Hausdorff, essentially principal, minimal and contracting, respectively. In some cases these conditions are in fact necessary and sufficient.The first-named author was partially supported by CNPq. The second-named author was partially supported by PAI III grants FQM-298 and P11-FQM-7156 of the Junta de Andalucía and by the DGI- MICINN and European Regional Development Fund, jointly, through Project MTM2011-28992-C02-02

Editorial: Integrative Computational Systems Biology Approaches in Immunology and Medicine

Editorial on the Research Topic "Integrative Computational Systems Biology Approaches in Immunology and Medicine

Beyond microarrays: Finding key transcription factors controlling signal transduction pathways

BACKGROUND: Massive gene expression changes in different cellular states measured by microarrays, in fact, reflect just an "echo" of real molecular processes in the cells. Transcription factors constitute a class of the regulatory molecules that typically require posttranscriptional modifications or ligand binding in order to exert their function. Therefore, such important functional changes of transcription factors are not directly visible in the microarray experiments. RESULTS: We developed a novel approach to find key transcription factors that may explain concerted expression changes of specific components of the signal transduction network. The approach aims at revealing evidence of positive feedback loops in the signal transduction circuits through activation of pathway-specific transcription factors. We demonstrate that promoters of genes encoding components of many known signal transduction pathways are enriched by binding sites of those transcription factors that are endpoints of the considered pathways. Application of the approach to the microarray gene expression data on TNF-alpha stimulated primary human endothelial cells helped to reveal novel key transcription factors potentially involved in the regulation of the signal transduction pathways of the cells. CONCLUSION: We developed a novel computational approach for revealing key transcription factors by knowledge-based analysis of gene expression data with the help of databases on gene regulatory networks (TRANSFAC(® )and TRANSPATH(®)). The corresponding software and databases are available at