Influence of Multi-Frequency Ultrasound Treatment on Conformational Characteristics of Beef Myofibrillar Proteins with Different Degrees of Doneness.

This study evaluated the effect of multi-frequency sonication (20 kHz, 25 kHz, 28 kHz, 40 kHz, 50 kHz) on structural characteristics of beef myofibrillar proteins (MPs) with different degrees of doneness (Rare 52~55 °C, Medium Rare 55~60 °C, Medium 60~65 °C, Medium Well 65~69 °C, Well Down 70~80 °C, and Overcooked 90 °C). The results showed that surface hydrophobicity and sulfhydryl content increased with the increase in degree of doneness. At the same degree of doneness, the sulfhydryl group contents reached the maximum at a frequency of 28 kHz. In addition, the absolute value of ζ-potential was significantly decreased after ultrasonic treatment (p < 0.05). SDS gel electrophoresis showed that the bands of beef MPs were not significantly affected by various ultrasonic frequencies, but the bands became thinner when the degree of doneness reached overcooked. Fourier transform infrared spectrum showed that with the increase of ultrasonic frequency, α-helix content decreased, and random coil content significantly increased (p < 0.05). The results of atomic force microscopy indicated that the surface structure of beef MPs was damaged, and the roughness decreased by sonication, while the roughness significantly increased when the degree of doneness changed from medium to overripe (p < 0.05). In conclusion, multi-ultrasound combined with degree of doneness treatment alters the structural characteristics of beef MPs.

Subject Separation Network for Reducing Calibration Time of MI-Based BCI.

Motor imagery brain-computer interface (MI-based BCIs) have demonstrated great potential in various applications. However, to well generalize classifiers to new subjects, a time-consuming calibration process is necessary due to high inter-subject variabilities of EEG signals. This process is costly and tedious, hindering the further expansion of MI-based BCIs outside of the laboratory. To reduce the calibration time of MI-based BCIs, we propose a novel domain adaptation framework that adapts multiple source subjects' labeled data to the unseen trials of target subjects. Firstly, we train one Subject Separation Network(SSN) for each of the source subjects in the dataset. Based on adversarial domain adaptation, a shared encoder is constructed to learn similar representations for both domains. Secondly, to model the factors that cause subject variabilities and eliminate the correlated noise existing in common feature space, private feature spaces orthogonal to the shared counterpart are learned for each subject. We use a shared decoder to validate that the model is actually learning from task-relevant neurophysiological information. At last, an ensemble classifier is built by the integration of the SSNs using the information extracted from each subject's task-relevant characteristics. To quantify the efficacy of the framework, we analyze the accuracy-calibration cost trade-off in MI-based BCIs, and theoretically guarantee a generalization bound on the target error. Visualizations of the transformed features illustrate the effectiveness of domain adaptation. The experimental results on the BCI Competition IV-IIa dataset prove the effectiveness of the proposed framework compared with multiple classification methods. We infer from our results that users could learn to control MI-based BCIs without a heavy calibration process. Our study further shows how to design and train Neural Networks to decode task-related information from different subjects and highlights the potential of deep learning methods for inter-subject EEG decoding.

A Novel Neurofeedback Attentional Enhancement Approach Based on Virtual Reality.

Attention enhancement can not only improve individual's study and work performance, but also help to improve such psychological problems as anxiety and depression. Traditional attention enhancement approaches have high requirements on the external environment, and thus have such limitations as long intervention periods, high costs, poor universality, and insignificant therapeutic effects. Virtual Reality (VR) can provide interactive and immersive environments, which can help to break through these limitations and effectively enhance users' attention. In this paper, we propose a novel neurofeedback attentional enhancement approach based on VR. The proposed approach utilizes the α band power in the parieto-occipital regions of the brain as an neurofeedback index of the users' attention, and prompts users by changing the attributes of the VR environment. Statistical results show that the α band power reduces significantly in neurofeedback group compared with that in control group. Accordingly, task performances in neruofeedback group are improved by 6.44% compared with those of control group. Our results provided evidence for the effectiveness of neurofeedback on VR training environment.

Neural Research on Depth Perception and Stereoscopic Visual Fatigue in Virtual Reality.

Virtual reality (VR) technology provides highly immersive depth perception experiences; nevertheless, stereoscopic visual fatigue (SVF) has become an important factor currently hindering the development of VR applications. However, there is scant research on the underlying neural mechanism of SVF, especially those induced by VR displays, which need further research. In this paper, a Go/NoGo paradigm based on disparity variations is proposed to induce SVF associated with depth perception, and the underlying neural mechanism of SVF in a VR environment was investigated. The effects of disparity variations as well as SVF on the temporal characteristics of visual evoked potentials (VEPs) were explored. Point-by-point permutation statistical with repeated measures ANOVA results revealed that the amplitudes and latencies of the posterior VEP component P2 were modulated by disparities, and posterior P2 amplitudes were modulated differently by SVF in different depth perception situations. Cortical source localization analysis was performed to explore the original cortex areas related to certain fatigue levels and disparities, and the results showed that posterior P2 generated from the precuneus could represent depth perception in binocular vision, and therefore could be performed to distinguish SVF induced by disparity variations. Our findings could help to extend an understanding of the neural mechanisms underlying depth perception and SVF as well as providing beneficial information for improving the visual experience in VR applications.

Amorphous metal-organic frameworks obtained from a crystalline precursor for the capture of iodine with high capacities.

Two amorphous metal-organic frameworks (aMOFs) were obtained from crystalline Co-MOF (SCNU-Z6) via temperature-induced (aT-SCNU-Z6) and water-immersed (aW-SCNU-Z6) approaches. They exhibited high iodine uptake, with the adsorption capacities of aT-SCNU-Z6 and aW-SCNU-Z6 reaching 2.05 and 5.04 g g-1, respectively. This work is the first report of iodine uptake by aMOFs.

Investigate the Neuro Mechanisms of Stereoscopic Visual Fatigue.

Stereoscopic visual fatigue (SVF) due to prolonged immersion in the virtual environment can lead to negative user experience, thus hindering the development of virtual reality (VR) industry. Previous studies have focused on investigating the evaluation indicators associated with SVF, while few studies have been conducted to reveal the underlying neural mechanism, especially in VR applications. In this paper, a modified Go/NoGo paradigm was adopted to induce SVF in VR environment with Go trials for maintaining participants' attention and NoGo trials for investigating the neural effects under SVF. Random dot stereograms (RDSs) with 11 disparities were presented to evoke the depth-related visual evoked potentials (DVEPs) during 64-channel EEG recordings. EEG datasets collected from 15 participants in NoGo trials were selected to conduct individual processing and group analysis, in which the characteristics of the DVEPs components for various fatigue degrees were compared and independent components were clustered to explore the original cortex areas related to SVF. Point-by-point permutation statistics revealed that DVEPs sample points from 230 ms to 280 ms (component P2) in most brain areas changed significantly when SVF increased. Additionally, independent component analysis (ICA) identified that component P2 which originated from posterior cingulate cortex and precuneus, was associated statistically with SVF. We believe that SVF is rather a conscious status concerning the changes of self-awareness or self-location awareness than the performance reduction of retinal image processing. Moreover, we suggest that indicators representing higher conscious state may be a better indicator for SVF evaluation in VR environments.