Bayesian models for finding and grouping junctions

In this paper, we propose two Bayesian methods for detecting and grouping junctions. Our junction detection method evolves from the Kona approach, and it is based on a competitive greedy procedure inspired in the region competition method. Then, junction grouping is accomplished by finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A* algorithm that has been recently proposed. Both methods are efficient and robust, and they are tested with synthetic and real images.

Gene cloning, molecular modeling, and phylogenetics of serine protease P32 and serine carboxypeptidase SCP1 from nematophagous fungi Pochonia rubescens and Pochonia chlamydosporia

The fungi Pochonia chlamydosporia and Pochonia rubescens are parasites of nematode eggs and thus are biocontrol agents of nematodes. Proteolytic enzymes such as the S8 proteases VCP1 and P32, secreted during the pathogenesis of nematode eggs, are major virulence factors in these fungi. Recently, expression of these enzymes and of SCP1, a new putative S10 carboxypeptidase, was detected during endophytic colonization of barley roots by these fungi. In our study, we cloned the genomic and mRNA sequences encoding P32 from P. rubescens and SCP1 from P. chlamydosporia. P32 showed a high homology with the serine proteases Pr1A from the entomopathogenic fungus Metarhizium anisopliae and VCP1 from P. chlamydosporia (86% and 76% identity, respectively). However, the catalytic pocket of P32 showed differences in the amino acids of the substrate-recognition sites compared with the catalytic pockets of Pr1A and VCP1 proteases. Phylogenetic analysis of P32 suggests a common ancestor with protease Pr1A. SCP1 displays the characteristic features of a member of the S10 family of serine proteases. Phylogenetic comparisons show that SCP1 and other carboxypeptidases from filamentous fungi have an origin different from that of yeast vacuolar serine carboxypeptidases. Understanding protease genes from nematophagous fungi is crucial for enhancing the biocontrol potential of these organisms.

Cancer mortality inequalities in urban areas: a Bayesian small area analysis in Spanish cities

Background: Intra-urban inequalities in mortality have been infrequently analysed in European contexts. The aim of the present study was to analyse patterns of cancer mortality and their relationship with socioeconomic deprivation in small areas in 11 Spanish cities. Methods: It is a cross-sectional ecological design using mortality data (years 1996-2003). Units of analysis were the census tracts. A deprivation index was calculated for each census tract. In order to control the variability in estimating the risk of dying we used Bayesian models. We present the RR of the census tract with the highest deprivation vs. the census tract with the lowest deprivation. Results: In the case of men, socioeconomic inequalities are observed in total cancer mortality in all cities, except in Castellon, Cordoba and Vigo, while Barcelona (RR = 1.53 95%CI 1.42-1.67), Madrid (RR = 1.57 95%CI 1.49-1.65) and Seville (RR = 1.53 95%CI 1.36-1.74) present the greatest inequalities. In general Barcelona and Madrid, present inequalities for most types of cancer. Among women for total cancer mortality, inequalities have only been found in Barcelona and Zaragoza. The excess number of cancer deaths due to socioeconomic deprivation was 16,413 for men and 1,142 for women. Conclusion: This study has analysed inequalities in cancer mortality in small areas of cities in Spain, not only relating this mortality with socioeconomic deprivation, but also calculating the excess mortality which may be attributed to such deprivation. This knowledge is particularly useful to determine which geographical areas in each city need intersectorial policies in order to promote a healthy environment.