The aim of this study is to investigate in vitro the anticancer, antioxidant, and antibacterial activities of three low molecular weight subfractions I, II and III isolated from secondary metabolites produced by the wood degrading fungus Cerrena unicolor. The present study demonstrated that the low molecular weight subfractions III exhibited the strongest inhibitory activity towards breast carcinoma cells MDA-MB-231, prostatic carcinoma cells PC3, and breast cancer cells MCF7 with the half-maximal inhibitory concentration (IC50) value of 52,25 μg/mL, 60,66 μg/mL, and 54,92 μg/mL, respectively. The highest percentage of inhibition was noted at a concentration of 300 μg/mL in all the examined tumor lines. A significant percentage (59.08%) of ex-LMSIII inhibition of the MDA-MB-231 tumor line was reached at a concentration of 15 μg/ml, while the concentration applied did not affect normal human fibroblast cells. The low molecular weight subfraction III was the most effective and additionally showed the highest free radical 1,1-diphenyl-2-picryl-hydrazyl scavenging activity (IC50 20.39 μg/mL) followed by the low molecular weight subfraction I (IC50 64.14 μg/mL) and II (IC50 49.22 μg/mL). The antibacterial activity of the tested preparations was evaluated against three microorganisms: Bacillus subtilis, Staphylococcus aureus, and Escherichia coli. The minimal inhibitory concentration (MIC) values for the low molecular weight subfraction I, II, and III showed a stronger inhibition effect on S. aureus than on B. subtilis and E. coli cells. The MIC values for the low molecular weight subfraction II against S. aureus, B. subtilis, and E. coli were 6.25, 12.5, and 100 mg/mL, respectively.

For the first time worldwide, innovative techniques, generic non-linear higher-order unnormalized cross-correlations of spectral moduli, for the diagnosis of complex assets, are proposed. The normalization of the proposed techniques is based on the absolute central moments, that have been proposed and widely investigated in mathematical works. The existing higher-order, cross-covariances of complex spectral components are not sufficiently effective. The novel technology is comprehensively experimentally validated for induction motor bearing diagnosis via motor current signals. Experimental results, provided by the proposed technique, confirmed high overall probabilities of correct diagnoses for bearings at early stages of damage development. The proposed diagnosis technology is compared with existing diagnosis technology, based on the triple cross-covariance of complex spectral components.

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It is proposed, developed, investigated, and validated by experiments and modelling for the first time in worldwide terms new data processing technologies, higher order spectral multiple correlation technologies for fault identification for electromechanical systems via electrical data processing. Investigation of the higher order spectral triple correlation technology via modelling has shown that the proposed data processing technology effectively detects component faults. The higher order spectral triple correlation technology successfully applied for rolling bearing fault identification. Experimental investigation of the technology has shown, that the technology effectively identifies rolling bearing fault by electrical data processing at very early stage of fault development. Novel technology comparisons via modelling and experiments of the proposed higher order spectral triple correlation technology and the higher order spectra technology show the higher fault identification effectiveness of the proposed technology over the bicoherence technology.

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