Biology subjects

Silva, A. C. d. S.

Publications and source records attributed to Silva, A. C. d. S..

3 recordsLinked to original sources

bioRxiv2021

Improving behavior monitoring of free moving dairy cows using noninvasive wireless EEG approach and digital signal processing techniques.

BackgroundElectroencephalography (EEG) is the most common method to access brain information. Techniques to monitor and to extract brain signal characteristics in farm animals are not as developed as in humans and laboratory animals. New methodThe method comprised two steps. In the first step, the signals were acquired after the telemetric equipment was developed, the electrodes were positioned and fixed, the sample frequency was defined, the equipment was positioned, and artifacts and other acquisition problems were dealt with. Brain signals from six Holstein heifers that could move freely in free stalls were acquired. The control group consisted in the same number of bovines, contained in a climatic chamber (restrained group). In the second step, the signals were characterized by Power Spectral Density, Short-Time Fourier Transform, and Lempel-Ziv complexity. ResultsThe results indicated that there was an ideal position to attach the electrodes to the front of the bovines head, so that longer artifact-free signal sections were acquired. The signals showed typical EEG frequency bands, like the bands found in humans. The Lempel-Ziv complexity values indicated that the bovine brain signals contained random and chaotic components. As expected, the signals acquired from the retained bovine group displayed sections with a larger number of artifacts. Comparison with existing methodsWe present the first method that helps to monitor and to extract brain signal features in unrestrained bovines. ConclusionsThe method could be applied to investigate changes in brain electrical activity during animal farming, to monitor brain activity related with animal behavior. HighlightsO_LIA method that allows brain signals to be monitored in freely moving dairy cows is described C_LIO_LIThe method uses noninvasive electrodes to minimize stress during EEG monitoring and allows bovines to behave normally during the process C_LIO_LIThe method establishes the frequency sampling rate, electrodes positioning and fixation, equipment holding, artifact extraction, and signal characterization C_LIO_LIThe brain signals are characterized by PSD, STFT, and Lempel-Ziv normalized complexity C_LIO_LIThe method can be applied to relate EEG to animal behavior under normal handling conditions C_LI

animal behavior and cognition↗

bioRxiv2021

Comparison of brain activity while tasting of passion fruit juice sweetened with caloric and non-caloric sweeteners

Sweetener type can influence sensory properties and consumers acceptance and preference for low-calorie products. An ideal sweetener does not exist, and each sweetener must be used in situations to which it is best suited. Aspartame and sucralose can be good substitutes for sucrose in passion fruit juice. Despite the interest in artificial sweeteners, little is known about how artificial sweeteners are processed in the human brain. Here, we evaluated brain signals of 11 healthy subjects when they tasted passion fruit juice equivalently sweetened with sucrose (9.4 g/100 g), sucralose (0.01593 g/100 g), or aspartame (0.05477 g/100 g). Electroencephalograms were recorded for two sites in the gustatory cortex (i.e., C3 and C4). Data with artifacts were disregarded, and the artifact-free data were used to feed a CNN. Our results indicated that the brain responses distinguish juice sweetened with different sweeteners with an average accuracy of 0.823. Practical ApplicationsFinding sweeteners that best fit consumer preferences evolves understanding how the gustatory cortex processes sweeteners. Ideal equivalence will occur when the brain is no longer able to distinguish stimuli that are consciously perceived. This study presents a method of signal acquisition using a single channel and an open-source processing environment. This would allow, for example, to disregard the use of a commercial electroencephalograph and expand the studies in this area and offering to food industry additional tools in the development of products sweetened with non-caloric sweeteners.

bioengineering↗

bioRxiv2020

Bioelectrical pattern discrimination of Miconia plants by spectral analysis and machine learning

We have conducted an in loco investigation into the species Miconia albicans (SW.) Triana and Miconia chamissois Naudin (Melastomataceae), distributed in different phytophysiognomies of three Cerrado fragments in the State of Sao Paulo, Brazil, to characterize their oscillatory bioelectrical signals and to find out whether these signals have distinct spectral density. The experiments provided a sample bank of bioelectrical amplitudes, which were analyzed in the time and frequency domain. On the basis of the power spectral density (PSD) and machine learning techniques, analyses in the frequency domain suggested that each species has a characteristic biological pattern. Comparison between the oscillatory behavior of the species clearly showed that they have bioelectrical features, that collecting data is feasible, that Miconia display a bioelectrical pattern, and that environmental factors influence this pattern. From the point of view of experimental Botany, new questions and concepts must be formulated to advance understanding of the interactions between the communicative nature of plants and the environment. The results of this on-site technique represent a new methodology to acquire non-invasive information that might be associated with physiological, chemical, and ecological aspects of plants. HighlightIn loco characterization of the bioelectrical signals of two Miconia species in the time and frequency domain suggests that the species have distinct biological patterns.

plant biology↗