Graphic-based concept retrieval

Two ways of expressing concepts in the context of image retrieval are presented. One, Keypics, is on the side of an image owner, who wants the image itself to be found on the Web; the second, Trittico, is on the side of the image searcher. Both are based on the paradigm of human intermediation for overcoming the semantic gap. Both require tools capable of qualitative analysis, and have been experimented by using persistent homology

Erythropoietin Improves the Survival of Fat Tissue after Its Transplantation in Nude Mice

Background: Autologous transplanted fat has a high resorption rate, providing a clinical challenge for the means to reduce it. Erythropoietin (EPO) has non-hematopoietic targets, and we hypothesized that EPO may improve long-term fat graft survival because it has both pro-angiogenic and anti-apoptotic properties. We aimed to determine the effect of EPO on the survival of human fat tissue after its transplantation in nude mice. Methodology/Principal Findings: Human fat tissue was injected subcutaneously into immunologically-compromised nude mice, and the grafts were then treated with either 20 IU or 100 IU EPO. At the end of the 15-week study period, the extent of angiogenesis, apoptosis, and histology were assessed in the fat grafts. The results were compared to vascular endothelial growth factor (VEGF)-treated and phosphate-buffered saline (PBS)-treated fat grafts. The weight and volume of the EPOtreated grafts were higher than those of the PBS-treated grafts, whose weights and volumes were not different from those of the VEGF-treated grafts. EPO treatment also increased the expression of angiogenic factors and microvascular density, and reduced inflammation and apoptosis in a dose-dependent manner in the fat grafts. Conclusions/Significance: Our data suggest that stimulation of angiogenesis by a cluster of angiogenic factors and decreased fat cell apoptosis account for potential mechanisms that underlie the improved long-term survival of fa

The Ras Antagonist, Farnesylthiosalicylic Acid (FTS), Decreases Fibrosis and Improves Muscle Strength in dy2J/dy2J Mouse Model of Muscular Dystrophy

The Ras superfamily of guanosine-triphosphate (GTP)-binding proteins regulates a diverse spectrum of intracellular processes involved in inflammation and fibrosis. Farnesythiosalicylic acid (FTS) is a unique and potent Ras inhibitor which decreased inflammation and fibrosis in experimentally induced liver cirrhosis and ameliorated inflammatory processes in systemic lupus erythematosus, neuritis and nephritis animal models. FTS effect on Ras expression and activity, muscle strength and fibrosis was evaluated in the dy2J/dy2J mouse model of merosin deficient congenital muscular dystrophy. The dy2J/dy2J mice had significantly increased RAS expression and activity compared with the wild type mice. FTS treatment significantly decreased RAS expression and activity. In addition, phosphorylation of ERK, a Ras downstream protein, was significantly decreased following FTS treatment in the dy2J/dy2J mice. Clinically, FTS treated mice showed significant improvement in hind limb muscle strength measured by electronic grip strength meter. Significant reduction of fibrosis was demonstrated in the treated group by quantitative Sirius Red staining and lower muscle collagen content. FTS effect was associated with significantly inhibition of both MMP-2 and MMP-9 activities. We conclude that active RAS inhibition by FTS was associated with attenuated fibrosis and improved muscle strength in the dy2J/dy2J mouse model of congenital muscular dystrophy

Spectral Log-Demons: Diffeomorphic Image Registration with Very Large Deformations

International audienceThis paper presents a new framework for capturing large and complex deformations in image registration and atlas construction. This challenging and recurrent problem in computer vision and medical imaging currently relies on iterative and local approaches, which are prone to local minima and, therefore, limit present methods to relatively small deformations. Our general framework introduces to this effect a new direct feature matching technique that finds global correspondences between images via simple nearest-neighbor searches. More specifically, very large image deformations are captured in Spectral Forces, which are derived from an improved graph spectral representation. We illustrate the benefits of our framework through a new enhanced version of the popular Log-Demons algorithm, named the Spectral Log-Demons, as well as through a groupwise extension, named the Groupwise Spectral Log-Demons, which is relevant for atlas construction. The evaluations of these extended versions demonstrate substantial improvements in accuracy and robustness to large deformations over the conventional Demons approaches

PROPER: global protein interaction network alignment through percolation matching

Background The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging. Results In this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria. Conclusions We highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch

Context sensitive search engine

In this paper, we use context information extracted from the documents in the collection to improve the performance of the search engine. In first step, we extract context using Lucene, DBPedia-Spotlight, and Wordnet. As the second step, we build a graph using extracted context information. In the third step, in order to group similar contexts, we cluster context graph. In the fourth step, we re-score results using context-clusters and context-information of documents, as well as queries. In the fifth step, we implement a data collection tool to collect gold-standard data. In the sixth and final step, we compare the results of our algorithm with gold-standard data set. According to the experimental results, using context information may improve the search engine performance but the collection should be relatively big.Publisher's Versio

What's new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval

Representation of influential entities, such as celebrities and multinational corporations on the web can vary across languages, re- flecting language-specific entity aspects, as well as divergent views on these entities in different communities. An important source of multilingual background knowledge about influential entities is Wikipedia — an online community-created encyclopaedia — containing more than 280 language editions. Such language-specific information could be applied in entity-centric information retrieval applications, in which users utilise very simple queries, mostly just the entity names, for the relevant documents. In this article we focus on the problem of creating languagespecific entity contexts to support entity-centric, language-specific information retrieval applications. First, we discuss alternative ways such contexts can be built, including Graph-based and Article-based approaches. Second, we analyse the similarities and the differences in these contexts in a case study including 220 entities and five Wikipedia language editions. Third, we propose a context-based entity-centric information retrieval model that maps documents to aspect space, and apply languagespecific entity contexts to perform query expansion. Last, we perform a case study to demonstrate the impact of this model in a news retrieval application. Our study illustrates that the proposed model can effectively improve the recall of entity-centric information retrieval while keeping high precision, and provide language-specific results