169,762 research outputs found
Time evolution of the microwave second-order response of YBaCuO powder
No full textTransient effects in the microwave second-order response of YBaCuO powder are investigated. The time evolution of the second-harmonic signal has been measured for about 300 s after the sample had been exposed to variations of the DC magnetic field. We show that in different time scales the transient response has different origin. In the time scale of milliseconds the transient response of samples in the critical state is ascribable to processes of flux redistribution induced by the switching on/off of the microwave field. At longer times, the time evolution of the second-harmonic signal can be ascribed to motion of fluxons induced by the variation of the DC magnetic field. In particular, diffusive motion of fluxons determines the response in the first 10 s after the stop of the magnetic field variation; magnetic relaxation over the surface barrier determines the response in the time scale of minutes. (C) 2003 Elsevier B.V. All rights reserve
Networks in biological systems: An investigation of the Gene Ontology as an evolving network
No full textMany biological systems can be described as networks where diFFerent elements interact, in order to perform biological processes. We introduce a network
associated with the Gene Ontology. Specifically, we construct a correlation-based network where the vertices are the terms of the Gene Ontology and the link between
each two terms is weighted on the basis of the number of genes that they have in
common. We analyze a filtered network obtained from the correlation-based network
and we characterize its evolution over different releases of the Gene Ontology
Gene-based and semantic structure of the Gene Ontology as a complex network
No full textThe last decade has seen the advent and consolidation of ontology based tools for the identification and biological interpretation of classes of genes, such as the Gene Ontology. The Gene Ontology (GO) is constantly evolving over time. The information accumulated time-by-time and included in the GO is encoded in the definition of terms and in the setting up of semantic relations amongst terms. Here we investigate the Gene Ontology from a complex network perspective. We consider the semantic network of terms naturally associated with the semantic relationships provided by the Gene Ontology consortium. Moreover, the GO is a natural example of bipartite network of terms and genes. Here we are interested in studying the properties of the projected network of terms, i.e. a gene-based weighted network of GO terms, in which a link between any two terms is set if at least one gene is annotated in both terms. One aim of the present paper is to compare the structural properties of the semantic and the gene-based network. The relative importance of terms is very similar in the two networks, but the community structure changes. We show that in some cases GO terms that appear to be distinct from a semantic point of view are instead connected, and appear in the same community when considering their gene content. The identification of such gene-based communities of terms might therefore be the basis of a simple protocol aiming at improving the semantic structure of GO. Information about terms that share large gene content might also be important from a biomedical point of view, as it might reveal how genes over-expressed in a certain term also affect other biological processes, molecular functions and cellular components not directly linked according to GO semantics
An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs
No full textMicroRNA are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR is a web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR was trained with the information regarding binding sites in the 3'utr region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein - a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3'utr information. On the other hand, we show that 3'utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3'utr and coding regions, should be considered in comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3'utr based one
miR-1207-5p Can Contribute to Dysregulation of Inflammatory Response in COVID-19 via Targeting SARS-CoV-2 RNA
No full textThe present study focuses on the role of human miRNAs in SARS-CoV-2 infection. An extensive analysis of human miRNA binding sites on the viral genome led to the identification of miR-1207-5p as potential regulator of the viral Spike protein. It is known that exogenous RNA can compete for miRNA targets of endogenous mRNAs leading to their overexpression. Our results suggest that SARS-CoV-2 virus can act as an exogenous competing RNA, facilitating the over-expression of its endogenous targets. Transcriptomic analysis of human alveolar and bronchial epithelial cells confirmed that the CSF1 gene, a known target of miR-1207-5p, is over-expressed following SARS-CoV-2 infection. CSF1 enhances macrophage recruitment and activation and its overexpression may contribute to the acute inflammatory response observed in severe COVID-19. In summary, our results indicate that dysregulation of miR-1207-5p-target genes during SARS-CoV-2 infection may contribute to uncontrolled inflammation in most severe COVID-19 cases
Identification of pathways involved in aneuploidy onset and its tolerance using a DNA microarray approach
No full textSparse inference of the human haematopoietic system from heterogeneous and partially observed genomic data
No full textHaematopoiesis is the process of blood cells’ formation, with progenitor stem cells differentiating into mature forms such as white and red blood cells or platelets. While progenitor cells share regulatory pathways involving common nuclear factors, specific networks shape their fate towards particular lineages. This paper analyses the complex regulatory network that drives the formation of mature red blood cells and platelets from their common precursors. Using the latest reverse transcription quantitative real-time PCR genomic data, we develop a dedicated graphical model that incorporates the effect of external genomic data and allows inference of regulatory networks from the high-dimensional and partially observed data
Spanning Trees and bootstrap reliability estimation in correlation based networks
No full textWe introduce a new technique to associate a spanning tree to the average linkage cluster analysis. We term this tree as the Average Linkage Minimum Spanning Tree. We also introduce a technique to associate a value of reliability to the links of correlation-based graphs by using bootstrap replicas of data. Both techniques are applied to the portfolio of the 300 most capitalized stocks traded on the New York Stock Exchange during the time period 2001-2003. We show that the Average Linkage Minimum Spanning Tree recognizes economic sectors and sub-sectors as communities in the network slightly better than the Minimum Spanning Tree. We also show that the average reliability of links in the Minimum Spanning Tree is slightly greater than the average reliability of links in the Average Linkage Minimum Spanning Tree
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