University of Ljubljana

About: University of Ljubljana is a education organization based out in Ljubljana, Slovenia. It is known for research contribution in the topics: Population & Liquid crystal. The organization has 17210 authors who have published 47013 publications receiving 1082684 citations. The organization is also known as: Univerza v Ljubljani.

Meta- and pooled analyses of the effects of glutathione S-transferase M1 polymorphisms and smoking on lung cancer risk

Abstract: Susceptibility to lung cancer may in part be attributable to inter-individual variability in metabolic activation or detoxification of tobacco carcinogens. The glutathione S-transferase M1 (GSTM1) genetic polymorphism has been extensively studied in this context; two recent meta-analyses of case-control studies suggested an association between GSTM1 deletion and lung cancer. At least 15 studies have been published after these overviews. We undertook a new meta-analysis to summarize the results of 43 published case-control studies including >18 000 individuals. A slight excess of risk of lung cancer for individuals with the GSTM1 null genotype was found (odds ratio (OR) = 1.17, 95% confidence interval (CI) 1.07-1.27). No evidence of publication bias was found (P = 0.4), however, it is not easy to estimate the extent of such bias and we cannot rule out some degree of publication bias in our results. A pooled analysis of the original data of about 9500 subjects involved in 21 case-control studies from the International Collaborative Study on Genetic Susceptibility to Environmental Carcinogens (GSEC) data set was performed to assess the role of GSTM1 genotype as a modifier of the effect of smoking on lung cancer risk with adequate power. Analyses revealed no evidence of increased risk of lung cancer among carriers of the GSTM1 null genotype (age-, gender- and center-adjusted OR = 1.08, 95% CI 0.98-1.18) and no evidence of interaction between GSTM1 genotype and either smoking status or cumulative tobacco consumption.

Fast algorithms for determining (generalized) core groups in social networks

Abstract: The structure of a large network (graph) can often be revealed by partitioning it into smaller and possibly more dense sub-networks that are easier to handle. One of such decompositions is based on "k-cores", proposed in 1983 by Seidman. Together with connectivity components, cores are one among few concepts that provide efficient decompositions of large graphs and networks. In this paper we propose an efficient algorithm for determining the cores decomposition of a given network with complexity $${\mathcal{O}(m)}$$ , where m is the number of lines (edges or arcs). In the second part of the paper the classical concept of k-core is generalized in a way that uses a vertex property function instead of degree of a vertex. For local monotone vertex property functions the corresponding generalized cores can be determined in $${\mathcal{O}(m\cdot\max(\Delta,\log{n}))}$$ time, where n is the number of vertices and Δ is the maximum degree. Finally the proposed algorithms are illustrated by the analysis of a collaboration network in the field of computational geometry.

Weighted multipoint interpolated DFT to improve amplitude estimation of multifrequency signal

Abstract: This paper describes the error reduction of frequency and amplitude estimates of the periodic signals with multipoint interpolated discrete Fourier transform (DFT). The bias removal and noise sensitivity properties of the interpolation algorithms are studied for rectangular and Hanning windows. The correction improves with increasing the number of the interpolation points of the DFT. The use of a suitable interpolation algorithm depends on the effective bits of the A/D conversion, on the position of the frequency component of the signal and on the mutual component interspacing along the frequency axis. Using different algorithms, we change adaptively the apparent window shape for the particular component.