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.

Tourism destination brand identity: The case of Slovenia

Abstract: This paper explores the concept of tourism destination brand identity from the supply-side perspective, in contrast to those studies that have focused on the demand-driven, tourists' perceived tourism destination brand image. Both researchers and practitioners have concluded that an analysis of the branding concept from both the identity and perceived-image perspective is essential and should be intertwined, where appropriate. This study, however, argues that investigations of tourism destination branding have primarily been conducted from a perceived-image perspective. Therefore, the dearth of studies offering an insight into the supply-side perspective may lead to an unbalanced view, misunderstandings and oversights concerning the possibilities and limitations of tourism destination branding. It introduces a theoretical framework designed to analyse tourism destination identity, particularly for the case study of Slovenia.

Phylogeography of a subterranean amphipod reveals cryptic diversity and dynamic evolution in extreme environments.

Abstract: Extreme conditions in subsurface are suspected to be responsible for morphological convergences, and so to bias biodiversity assessment. Subterranean organisms are also considered as having poor dispersal abilities that in turn generate a large number of endemic species when habitat is fragmented. Here we test these general hypotheses using the subterranean amphipod Niphargus virei. All our phylogenetic analyses (Bayesian, maximum likelihood and distance), based on two independent genes (28S and COI), revealed the same tripartite structure. N. virei populations from Benelux, Jura region and the rest of France appeared as independent evolutionary units. Molecular rates estimated via global or Bayesian relaxed clock suggest that this split is at least 13 million years old and accredit the cryptic diversity hypothesis. Moreover, the geographical distribution of these lineages showed some evidence of recent dispersal through apparent vicariant barrier. In consequence, we argue that future analyses of evolution and biogeography in subsurface, or more generally in extreme environments, should consider dispersal ability as an evolving trait and morphology as a potentially biased marker.

Class prediction for high-dimensional class-imbalanced data

Abstract: The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance. Our results show that the evaluated classifiers are highly sensitive to class imbalance and that variable selection introduces an additional bias towards classification into the majority class. Most new samples are assigned to the majority class from the training set, unless the difference between the classes is very large. As a consequence, the class-specific predictive accuracies differ considerably. When the class imbalance is not too severe, down-sizing and asymmetric bagging embedding variable selection work well, while over-sampling does not. Variable normalization can further worsen the performance of the classifiers. Our results show that matching the prevalence of the classes in training and test set does not guarantee good performance of classifiers and that the problems related to classification with class-imbalanced data are exacerbated when dealing with high-dimensional data. Researchers using class-imbalanced data should be careful in assessing the predictive accuracy of the classifiers and, unless the class imbalance is mild, they should always use an appropriate method for dealing with the class imbalance problem.

Data Fusion by Matrix Factorization

Abstract: For most problems in science and engineering we can obtain data sets that describe the observed system from variousperspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly associated data together with contextual data and data about system’s constraints. In the paper we describe a data fusion approach with penalized matrix tri-factorization (DFMF) that simultaneouslyfactorizes data matrices to reveal hidden associations. The approach can directly consider any data that can be expressed in a matrix, including those from feature-based representations, ontologies, associations and networks. We demonstrate the utility of DFMF for gene function prediction task with eleven different data sources and for prediction of pharmacologic actions by fusing six data sources. Our data fusion algorithm compares favorably to alternative data integration approaches and achieves higher accuracy than can be obtained from any single data source alone.

An Overview of Conventional and Emerging Analytical Methods for the Determination of Mycotoxins

Abstract: Mycotoxins are a group of compounds produced by various fungi and excreted into the matrices on which they grow, often food intended for human consumption or animal feed. The high toxicity and carcinogenicity of these compounds and their ability to cause various pathological conditions has led to widespread screening of foods and feeds potentially polluted with them. Maximum permissible levels in different matrices have also been established for some toxins. As these are quite low, analytical methods for determination of mycotoxins have to be both sensitive and specific. In addition, an appropriate sample preparation and pre-concentration method is needed to isolate analytes from rather complicated samples. In this article, an overview of methods for analysis and sample preparation published in the last ten years is given for the most often encountered mycotoxins in different samples, mainly in food. Special emphasis is on liquid chromatography with fluorescence and mass spectrometric detection, while in the field of sample preparation various solid-phase extraction approaches are discussed. However, an overview of other analytical and sample preparation methods less often used is also given. Finally, different matrices where mycotoxins have to be determined are discussed with the emphasis on their specific characteristics important for the analysis (human food and beverages, animal feed, biological samples, environmental samples). Various issues important for accurate qualitative and quantitative analyses are critically discussed: sampling and choice of representative sample, sample preparation and possible bias associated with it, specificity of the analytical method and critical evaluation of results.