ERTNet: an interpretable transformer-based framework for EEG emotion recognition.

Background: Emotion recognition using EEG signals enables clinicians to assess patients' emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy. Methods: We developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state. Results: Experiments' results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data. Discussion: Given its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface.

Obesogenic effect of Bisphenol P on mice via altering the metabolic pathways.

Bisphenol P (BPP), structurally similar to bisphenol A, is commonly identified in the samples of environment, food, and humans. Unfortunately, very little information is currently available on adverse effects of BPP. The obesogenic effects and underlying mechanisms of BPP on mice were investigated in this study. Compared with the control, high-resolution microcomputed tomography (micro-CT) scans displayed that the visceral fat volume of mice was significantly increased at a dose of 5 mg/kg/day after BPP exposure for 14 days, whereas the subcutaneous fat volume remained unchanged. Nontargeted metabolomic analysis revealed that BPP significantly perturbed the metabolic pathways of mouse livers, and acetyl-CoA was identified as the potential key metabolite responsible for the visceral fat induced by BPP. These findings recommend that a great deal of attention should be paid to the obesogenic properties of BPP as a result of its widely utilized and persistence in the environment.

Adsorption of estrone with few-layered boron nitride nanosheets: Kinetics, thermodynamics and mechanism.

Boron nitride Nanosheets (BNNSs) was fabricated with a method of heating the mixture of boric acid and urea in N2 atmosphere and used to remove estrone (E1) from water. The obtained BNNSs exhibited a higher surface area of 896 m2/g, a large pore volume of 0.76 cm3/g, and only few layers (0.398 nm) with the boric acid and urea ratio of 1:80. The layer number of BNNSs decreased from 15 to 4 with the mole ratio of boric acid and urea decreasing from 1:20 to 1:80, which was identified by SEM, TEM, AFM and BET measurements. More importantly, BNNSs presented an outstanding adsorption performance for estrone with the adsorption capacity of 249.15 mg E1/g BNNSs. The adsorption process could be best fitted by pseudo second-order kinetic model and the equilibrium data at different temperatures were well fitted by Langmuir isotherm model. The thermodynamics analysis revealed that E1 adsorption on BNNSs was spontaneous (ΔG = -29.33 kJ mol-1), enthalpy-retarded (ΔH = 29.75 kJ mol-1), entropy-driven (ΔS = 198.26 J mol-1 K-1), and mostly chemical adsorption. The adsorption rates of E1 in water were sharply enhanced with thinner BNNSs as absorbents and removal efficiency by BN-60 regenerated after 6 times was above 95%, it was shown that the surface areas, mesopores and remarkable structure played important roles in the adsorption process. The firmness of E1 onto BNNSs and the stability of adsorption efficiency made BNNSs as a potential absorbent for efficient removal of E1 from wastewater.

Facile Selective and Diverse Fabrication of Superhydrophobic, Superoleophobic-Superhydrophilic and Superamphiphobic Materials from Kaolin.

As the starting material, kaolin is selectively and diversely fabricated to the superhydrophobic, superoleophobic-superhydrophilic, and superamphiphobic materials, respectively. The wettability of the kaolin surface can be selectively controlled and regulated to different superwetting states by choosing the corresponding modification reagent. The procedure is facile to operate, and no special technique or equipment is required. In addition, the procedure is cost-effective and time-saving and the obtained super-repellent properties are very stable. The X-ray photoelectron spectroscopy analysis demonstrates different changes of kaolin particles surfaces which are responsible for the different super-repellency. The scanning electron microscopy displays geometric micro- and nanometer structures of the obtained three kinds of super-repellent materials. The results show that kaolin has good applications in many kinds of superwetting materials. The method demonstrated in this paper provides a new strategy for regulating and controlling the wettability of solid surfaces selectively, diversely, and comprehensively.