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.

Association of dietary type with fecal microbiota in vegetarians and omnivores in Slovenia

Abstract: The purpose of this study was to discover differences in the human fecal microbiota composition driven by long-term omnivore versus vegan/lacto-vegetarian dietary pattern. In addition, the possible association of demographic characteristics and dietary habits such as consumption of particular foods with the fecal microbiota was examined. This study was conducted on a Slovenian population comprising 31 vegetarian participants (11 lacto-vegetarians and 20 vegans) and 29 omnivore participants. Bacterial DNA was extracted from the frozen fecal samples by Maxwell 16 Tissue DNA Purification Kit (Promega). Relative quantification of selected bacterial groups was performed by real-time PCR. Differences in fecal microbiota composition were evaluated by PCR–DGGE fingerprinting of the V3 16S rRNA region. Participants’ demographic characteristics, dietary habits and health status information were collected through a questionnaire. Vegetarian diet was associated with higher ratio (% of group-specific DNA in relation to all bacterial DNA) of Bacteroides–Prevotella, Bacteroides thetaiotaomicron, Clostridium clostridioforme and Faecalibacterium prausnitzii, but with lower ratio (%) of Clostridium cluster XIVa. Real-time PCR also showed a higher concentration and ratio of Enterobacteriaceae (16S rDNA copies/g and %) in female participants (p < 0.05 and p < 0.01) and decrease in Bifidobacterium with age (p < 0.01). DGGE analysis of the 16S rRNA V3 region showed that relative quantity of DGGE bands from certain bacterial groups was lower (Bifidobacterium, Streptococus, Collinsella and Lachnospiraceae) or higher (Subdoligranulum) among vegetarians, indicating the association of dietary type with bacterial community composition. Sequencing of selected DGGE bands revealed the presence of common representatives of fecal microbiota: Bacteroides, Eubacterium, Faecalibacterium, Ruminococcaceae, Bifidobacterium and Lachnospiraceae. Up to 4 % of variance in microbial community analyzed by DGGE could be explained by the vegetarian type of diet. Long-term vegetarian diet contributed to quantity and associated bacterial community shifts in fecal microbiota composition. Consumption of foods of animal origin (eggs, red meat, white meat, milk, yoghurt, other dairy products, fish and seafood) and vegetarian type of diet explained the largest share of variance in microbial community structure. Fecal microbiota composition was also associated with participants’ age, gender and body mass.

Analyzing Attribute Dependencies

Abstract: Many effective and efficient learning algorithms assume independence of attributes. They often perform well even in domains where this assumption is not really true. However, they may fail badly when the degree of attribute dependencies becomes critical. In this paper, we examine methods for detecting deviations from independence. These dependencies give rise to “interactions” between attributes which affect the performance of learning algorithms. We first formally define the degree of interaction between attributes through the deviation of the best possible “voting” classifier from the true relation between the class and the attributes in a domain. Then we propose a practical heuristic for detecting attribute interactions, called interaction gain. We experimentally investigate the suitability of interaction gain for handling attribute interactions in machine learning. We also propose visualization methods for graphical exploration of interactions in a domain.

Simplified non-linear seismic analysis of infilled reinforced concrete frames

Abstract: The N2 method for simplified non-linear seismic analysis has been extended in order to make it applicable to infilled reinforced concrete frames. Compared to the simple basic variant of the N2 method, two important differences apply. A multi-linear idealization of the pushover curve, which takes into account the strength degradation which occurs after the infill fails, has to be made, and specific reduction factors, developed in a companion paper, have to be used for the determination of inelastic spectra. It is shown that the N2 method can also be used for the determination of approximate summarized IDA curves. The proposed method was applied to two test buildings. The results were compared with the results obtained by non-linear dynamic analyses for three sets of ground motions, and a reasonable accuracy was demonstrated. A similar extension of the N2 method can be made to any structural system, provided that an appropriate specific R–µ–T relation is available. Copyright © 2004 John Wiley & Sons, Ltd.