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Ganoderma, often hailed as a holistic "health package", comprises an array of nutritional components and active compounds, contributing to its esteemed status in the realm of healthy foods. In this study, a comprehensive analysis was performed to elucidate the diverse nutritional profiles, bioactive components, and antiproliferative activities between two Ganoderma species: G. lucidum (GLU) and G. leucocontextum (GLE). The results showed that GLE possessed a higher level of nutritional constituents, except for dietary fiber. Fatty acid analysis revealed comparable profiles rich in unsaturated fatty acids for both species. The ethanol extract of GLU and GLE exhibited potent antioxidant capabilities and remarkable inhibition of tumor cell proliferation via apoptosis induction, with greater potency in GLE. The heightened triterpene levels in GLE potentially contribute to its augmented antitumoral effects. The exploration emphasized the significance of comprehending the varied chemical compositions of Ganoderma species, providing insights into their potential health benefits applications in the food and pharmaceutical industries.

RESUMEN

Electroencephalogram (EEG)-based emotion computing has become a hot topic of brain-computer fusion. EEG signals have inherent temporal and spatial characteristics. However, existing studies did not fully consider the two properties. In addition, the position encoding mechanism in the vanilla transformer cannot effectively encode the continuous temporal character of the emotion. A temporal relative (TR) encoding mechanism is proposed to encode the temporal EEG signals for constructing the temporality self-attention in the transformer. To explore the contribution of each EEG channel corresponding to the electrode on the cerebral cortex to emotion analysis, a channel-attention (CA) mechanism is presented. The temporality self-attention mechanism cooperates with the channel-attention mechanism to utilize the temporal and spatial information of EEG signals simultaneously by preprocessing. Exhaustive experiments are conducted on the DEAP dataset, including the binary classification on valence, arousal, dominance, and liking. Furthermore, the discrete emotion category classification task is also conducted by mapping the dimensional annotations of DEAP into discrete emotion categories (5-class). Experimental results demonstrate that our model outperforms the advanced methods for all classification tasks.

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In this study, a new model for predicting preterm delivery (PD) was proposed. The primary model was constructed using ten selected variables, as previously defined in seventeen different studies. The ability of the model to predict PD was evaluated using the combined measurement from these variables. Therefore, a prospective investigation was performed by enrolling 130 pregnant patients whose gestational ages varied from 17+0 to 28+6 weeks. The patients underwent epidemiological surveys and ultrasonographic measurements of their cervixes, and cervicovaginal fluid and serum were collected during a routine speculum examination performed by the managing gynecologist. The results showed eight significant variables were included in the present analysis, and combination of the positive variables indicated an increased probability of PD in pregnant patients. The accuracy for predicting PD were as follows: one positive - 42.9%; two positives - 75.0%; three positives - 81.8% and four positives - 100.0%. In particular, the combination of ≥2× positives had the best predictive value, with a relatively high sensitivity (82.6%), specificity (88.1%) and accuracy rate (79.2%), and was considered the cut-off point for predicting PD. In conclusion, the new model provides a useful reference for evaluating the risk of PD in clinical cases.

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