Jožef Stefan Institute

About: Jožef Stefan Institute is a facility organization based out in Ljubljana, Slovenia. It is known for research contribution in the topics: Liquid crystal & Dielectric. The organization has 3828 authors who have published 12614 publications receiving 291025 citations.

Novel Mechanism of Cathepsin B Inhibition by Antibiotic Nitroxoline and Related Compounds

Abstract: A new trick for an old dog! Aberrant cathepsin B activity is associated with tumor progression, however, despite extensive research, there are no cathepsinB inhibitors in clinical use. Here, nitroxoline, an established antimicrobial agent, is identified as a potent, reversible inhibitor of cathepsin B, and is thus a potential drug candidate for the treatment of cancer and other diseases in which cathepsin B activity plays a role.

Cell membrane fluidity related to electroporation and resealing

Abstract: In this paper, we report the results of a systematic attempt to relate the intrinsic plasma membrane fluidity of three different cell lines to their electroporation behaviour, which consists of reversible and irreversible electroporation. Apart from electroporation behaviour of given cell lines the time course required for membrane resealing was determined in order to distinguish the effect of resealing time from the cell’s ability to survive given electric pulse parameters. Reversible, irreversible electroporation and membrane resealing were then related to cell membrane fluidity as determined by electron paramagnetic resonance spectroscopy and computer characterization of membrane domains. We found that cell membrane fluidity does not have significant effect on reversible electroporation although there is a tendency for the voltage required for reversible electroporation to increase with increased membrane fluidity. Cell membrane fluidity, however, may affect irreversible electroporation. Nevertheless, this effect, if present, is masked with different time courses of membrane resealing found for the different cell lines studied. The time course of cell membrane resealing itself could be related to the cell’s ability to survive.

First order random forests: Learning relational classifiers with complex aggregates

Abstract: In relational learning, predictions for an individual are based not only on its own properties but also on the properties of a set of related individuals. Relational classifiers differ with respect to how they handle these sets: some use properties of the set as a whole (using aggregation), some refer to properties of specific individuals of the set, however, most classifiers do not combine both. This imposes an undesirable bias on these learners. This article describes a learning approach that avoids this bias, using first order random forests. Essentially, an ensemble of decision trees is constructed in which tests are first order logic queries. These queries may contain aggregate functions, the argument of which may again be a first order logic query. The introduction of aggregate functions in first order logic, as well as upgrading the forest's uniform feature sampling procedure to the space of first order logic, generates a number of complications. We address these and propose a solution for them. The resulting first order random forest induction algorithm has been implemented and integrated in the ACE-ilProlog system, and experimentally evaluated on a variety of datasets. The results indicate that first order random forests with complex aggregates are an efficient and effective approach towards learning relational classifiers that involve aggregates over complex selections.

Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform

Abstract: Sentiment analysis from data streams is aimed at detecting authors’ attitude, emotions and opinions from texts in real-time. To reduce the labeling effort needed in the data collection phase, active learning is often applied in streaming scenarios, where a learning algorithm is allowed to select new examples to be manually labeled in order to improve the learner’s performance. Even though there are many on-line platforms which perform sentiment analysis, there is no publicly available interactive on-line platform for dynamic adaptive sentiment analysis, which would be able to handle changes in data streams and adapt its behavior over time. This paper describes ClowdFlows, a cloud-based scientific workflow platform, and its extensions enabling the analysis of data streams and active learning. Moreover, by utilizing the data and workflow sharing in ClowdFlows, the labeling of examples can be distributed through crowdsourcing. The advanced features of ClowdFlows are demonstrated on a sentiment analysis use case, using active learning with a linear Support Vector Machine for learning sentiment classification models to be applied to microblogging data streams.