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Tonometry is commonly used to provide efficient and good diagnostics for cardiovascular disease (CVD). There are many advantages of this method, including low cost, non-invasiveness and little time to perform. In this study, the effort was undertaken to check whether tonometry data hides valuable information associated with different stages of chronic kidney disease (CKD) and end-stage renal disease (ESRD) treatment. For this purpose, six groups containing patients at different stages of CKD following different ways of dialysis treatment, as well as patients without CKD but with CVD and healthy volunteers were assessed. It was revealed that each of the studied groups had a unique profile. Only the type of dialysis was indistinguishable a from tonometric perspective (hemodialysis vs. peritoneal dialysis). Several techniques were used to build profiles that independently gave the same outcome: analysis of variance, network correlation structure analysis, multinomial logistic regression, and discrimination analysis. Moreover, to evaluate the classification potential of the discriminatory model, all mentioned techniques were later compared and treated as feature selection methods. Although the results are promising, it could be difficult to express differences as simple mathematical relations. This study shows that artificial intelligence can differentiate between different stages of CKD and patients without CKD. Potential future machine learning models will be able to determine kidney health with high accuracy and thereby classify patients. ClinicalTrials.gov Identifier: NCT05214872.

W dobie zagrożeń asymetrycznych cyberbezpieczeństwo infrastruktury krytycznej staje się poważną kwestią, a jednocześnie wyzwaniem dla twórców systemów zabezpieczeń. W niniejszym artykule przedstawiono czynniki eskalujące poziom trudności detekcji zaawansowanych zagrożeń, a także, na przykładzie dwóch projektów naukowo-badawczych, opisano realizowane przez Poznańskie Centrum Superkomputerowo-Sieciowe (PCSS) prace podejmujące to wyzwanie. Na przykładzie krajowego projektu SCADvance opisano zastosowanie algorytmów uczenia maszynowego do wykrywania zagrożeń w protokołach sieci przemysłowych. Wskazano również na rolę, jaką środowisko naukowe jest w stanie odegrać w tworzeniu innowacyjnych systemów zabezpieczeń infrastruktury krytycznej, a także na konieczność zastosowania rozwiązań tej klasy dla właściwej ochrony wrażliwych sieci teleinformatycznych.

Among acarid mites, a number of species are characterised by the presence of discontinuous morphologies (armed heteromorphs vs. unarmed homeomorphs) associated with alternative mating tactics (fighting vs. scramble competition). In Rhizoglyphus echinopus, expression of the fighter morph is suppressed, via pheromones, in large, dense colonies. If this mechanism is adaptive, fighters should have relatively lower fitness in large and/or dense colonies, due to costs incurred from fighting, which is often fatal. In order to test these predictions, we quantified the survival and mating success of fighters and scramblers in colonies of equal sex and morph ratios; these colonies either differed in size (4, 8, or 32 individuals) but not density or differed in density but not size (all consisted of 8 individuals). We found that the relative survival and mating success of fighters was inversely related to colony size, but we did not find a significant effect of colony density. The higher mating success of fighters in small colonies was due to the fact that, after killing rival males, these fighters were able to monopolise females. This situation was not found in larger colonies, in which there was a larger number of competitors and fighters suffered relatively higher mortality. These results indicate that morph determination, guided by social cues, allows for the adaptive adjustment of mating tactics to existing demographic conditions. Electronic supplementary material The online version of this article (doi:10.1007/s00265-014-1787-7) contains supplementary material, which is available to authorized users.