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Question: Is it possible to mathematically classify relevés into vegetation types on the basis of their average indicator values, including the uncertainty of the classification? Location: The Netherlands. Method: A large relevé database was used to develop a method for predicting vegetation types based on indicator values. First, each relevé was classified into a phytosociological association on the basis of its species composition. Additionally, mean indicator values for moisture, nutrients and acidity were computed for each relevé. Thus, the position of each classified relevé was obtained in a three-dimensional space of indicator values. Fitting the data to so called Gaussian Mixture Models yielded densities of associations as a function of indicator values. Finally, these density functions were used to predict the Bayesian occurrence probabilities of associations for known indicator values. Validation of predictions was performed by using a randomly chosen half of the database for the calibration of densities and the other half for the validation of predicted associations. Results and Conclusions:With indicator values, most relevés were classified correctly into vegetation types at the association level. This was shown using confusion matrices that relate (1) the number of relevés classified into associations based on species composition to (2) those based on indicator values. Misclassified relevés belonged to ecologically similar associations. The method seems very suitable for predictive vegetation models.Keywords: Environmental Impact Assessment; Modelling; Phytosociology; Predictive vegetation model. Abbreviations: iv = mean indicator value; pdf = probability density function; P = occurrence probability; C k = vegetation type; GMM = Gaussian Mixture Model.

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Background Hepatocellular carcinoma (HCC) is one of the most common malignancies, with an increasing incidence. Despite the fact that systematic chemotherapy with a doxorubicin provides only marginal improvements in survival of the HCC patients, the doxorubicin is being used in transarterial therapies or combined with the target drug – sorafenib. The aim of the study was to evaluate the effect of natural flavonoids on the cytotoxicity of the doxorubicin against human hepatocellular carcinoma cell line HepG2. Methods The effect of apigenin and its glycosides - cosmosiin, rhoifolin; baicalein and its glycosides – baicalin as well as hesperetin and its glycosides – hesperidin on glycolytic genes expression of HepG2 cell line, morphology and cells’ viability at the presence of doxorubicin have been tested. In an attempt to elucidate the mechanism of observed results, the fluorogenic probe for reactive oxygen species (ROS), the DNA oxidative damage, the lipid peroxidation and the double strand breaks were evaluated. To assess impact on the glycolysis pathway, the mRNA expression for a hexokinase 2 (HK2) and a lactate dehydrogenase A (LDHA) enzymes were measured. The results were analysed statistically with the one-way analysis of variance (ANOVA) and post hoc multiple comparisons. Results The apigenin and the hesperidin revealed the strongest effect on the toxicity of doxorubicin. Both flavonoids simultaneously changed the expression of the glycolytic pathway genes - HK2 and LDHA , which play a key role in the Warburg effect. Although separate treatment with doxorubicin, apigenin and hesperidin led to a significant oxidative DNA damage and double strand breaks, simultaneous administration of doxorubicin and apigenin or hesperidin abolished these damage with the simultaneous increase in the doxorubicin toxicity. Conclusion The obtained results indicate the existence of a very effective cytotoxic mechanism in the HepG2 cells of the combined effect of doxorubicin and apigenin (or hesperidin), not related to the oxidative stress. To explain this synergy mechanism, further research is needed, The observed intensification of the cytotoxic effect of doxorubicin by this flavonoids may be a promising direction of the research on the therapy of hepatocellular carcinoma, especially in a chemoembolization.