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The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

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Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively.

Exposure to Ultraviolet (UV) light induces photoaging of skin, leading to wrinkles and sunburn. The perennial herb Humulus japonicus, widely distributed in Asia, is known to have antiinflammatory, antimicrobial, and antioxidant effects. However, the physiological activities of isolated compounds from H. japonicus have rarely been investigated. This study focused on the isolation of active compounds from H. japonicus and the evaluation of their effects on photoaging in UVB-irradiated human fibroblast (Hs68) cells. When the extract and four fractions of H. japonicus were treated respectively in UVB-irradiated Hs68 cells to investigate anti-photoaging effects, the ethyl acetate (EtOAc) fraction showed the strongest inhibitory effect on MMP-1 secretion. From EtOAc fraction, we isolated luteolin-8-C-glucoside (1), apigenin-8-C-glucoside (2), and luteolin-7-O-glucoside (3). These compounds suppressed UVB-induced MMP-1 production by inhibiting the phosphorylation of the mitogen-activated protein kinases (MAPKs) and activator protein-1 (AP-1). When the antioxidant activity of the compounds were estimated by conducting western blot, calculating the bond dissociation energies of the O-H bond (BDE) at different grade, and measuring radical scavenging activity, we found luteolin-8-C-glucoside (1) showed the strongest activity on the suppression of UVB-induced photoaging. These results demonstrate the inhibitory effect of three flavone glycosides derived from H. japonicus on MMP-1 production, MAPK and AP-1 signaling, and oxidative stress; this could prove useful in suppressing UVB induced photoaging.