This work has been carried out to support the investigation of the electroencephalogram (EEG) Fourier power spectral, coherence, and detrended fluctuation characteristics during performance of mental tasks. To this aim, the presented dataset contains International 10/20 system EEG recordings from subjects under mental cognitive workload (performing mental serial subtraction) and the corresponding reference background EEGs. Based on the subtraction task performance (number of subtractions and accuracy of the result), the subjects were divided into good counters and bad counters (for whom the mental task required excessive efforts). The data was recorded from 36 healthy volunteers of matched age, all of whom are students of Educational and Scientific Centre “Institute of Biology and Medicine”, National Taras Shevchenko University of Kyiv (Ukraine); the recordings are available through Physiobank platform. The dataset can be used by the neuroscience research community studying brain dynamics during cognitive workload.

In the study of human cognitive activity using electroencephalogram (EEG), the brain dynamics parameters and characteristics play a crucial role. They allow to investigate the changes in functionality depending on the environment and task performance process, and also to access the intensity of the brain activity in various locations of the cortex and its dependencies. Usually, the dynamics of activation of different brain areas during the cognitive tasks are being studied by spectral analysis based on power spectral density (PSD) estimation, and coherence analysis, which are de facto standard tools in quantitative characterization of brain activity. PSD and coherence reflect the strength of oscillations and similarity of the emergence of these oscillations in the brain, respectively, while the concept of stability of brain activity over time is not well defined and less formalized. We propose to employ the detrended fluctuation analysis (DFA) as a measure of the EEG persistence over time, and use the DFA scaling exponent as its quantitative characteristics. We applied DFA to the study of the changes in activation in brain dynamics during mental calculations and united it with PSD and coherence estimation. In the experiment, EEGs during resting state and mental serial subtraction from 36 subjects were recorded and analyzed in four frequency ranges: θ1 (4.1–5.8 Hz), θ2 (5.9–7.4 Hz), β1 (13–19.9 Hz), and β2 (20–25 Hz). PSD maps to access the intensity of cortex activation and coherence to quantify the connections between different brain areas were calculated, the distribution of DFA scaling exponent over the head surface was exploited to measure the time characteristics of the dynamics of brain activity. Obtained arrangements of DFA scaling exponent suggest that normal functioning of the brain is characterized by long-term temporal correlations in the cortex. Topographical distribution of the DFA scaling exponent was comparable for θ and β frequency bands, demonstrating the largest values of DFA scaling exponent during cognitive activation. The study shows that the long-term temporal correlations evaluated by DFA can be of great interest for diagnosis of the variety of brain dysfunctions of different etiology in the future.

Ag–CeO2 catalysts (20 mol % Ag) were synthesized using different techniques (co-precipitation, impregnation, and impregnation of pre-reduced ceria), characterized by XRD, N2 sorption, TEM, H2-TPR methods, and probed in room-temperature p-nitrophenol reduction into p-aminophenol in aqueous solution at atmospheric pressure. The catalyst preparation method was found to determine the textural characteristics, the oxidation state and distribution of silver and, hence, the catalytic activity in the p-nitrophenol reduction. The impregnation technique was the most favorable for the formation over the ceria surface of highly dispersed silver species that are active in the p-nitrophenol reduction (the first-order rate constant k = 0.656 min−1).

A new water-soluble thermosensitive star-like copolymer, dextran-graft-poly-N-iso-propilacrylamide (D-g-PNIPAM), was created and characterized by various techniques (size-exclusion chromatography, differential scanning calorimetry, Fourier-transform infrared (FTIR) spectroscopy, and dynamic light scattering (DLS) spectroscopy). The viability of cancer cell lines (human transformed cervix epithelial cells, HeLa) as a model for cancer cells was studied using MTT and Live/Dead assays after incubation with a D-g-PNIPAM copolymer as a carrier for the drug doxorubicin (Dox) as well as a D-g-PNIPAM + Dox mixture as a function of the concentration. FTIR spectroscopy clearly indicated the complex formation of Dox with the D-g-PNIPAM copolymer. The size distribution of particles in Hank’s solution was determined by the DLS technique at different temperatures. The in vitro uptake of the studied D-g-PNIPAM + Dox nanoparticles into cancer cells was demonstrated by confocal laser scanning microscopy. It was found that D-g-PNIPAM + Dox nanoparticles in contrast to Dox alone showed higher toxicity toward cancer cells. All of the aforementioned facts indicate a possibility of further preclinical studies of the water-soluble D-g-PNIPAM particles’ behavior in animal tumor models in vivo as promising carriers of anticancer agents.

Understanding brain reactions to facial expressions can help in explaining emotion-processing and memory mechanisms. The purpose of this research is to examine the dynamics of electrical brain activity caused by visual emotional stimuli. The focus is on detecting changes in cognitive mechanisms produced by negative, positive, and neutral expressions on human faces. Three methods were used to study brain reactions: power spectral density, detrending moving average (DMA), and coherence analysis. Using electroencephalogram (EEG) recordings from 48 subjects while presenting facial image stimuli from the International Affective Picture System, the topographic representation of the evoked responses was acquired and evaluated to disclose the specific EEG-based activity patterns in the cortex. The theta and beta systems are two key cognitive systems of the brain that are activated differently on the basis of gender. The obtained results also demonstrate that the DMA method can provide information about the cortical networks’ functioning stability, so it can be coupled with more prevalent methods of EEG analysis.