The Formation of D-Allulose 3-Epimerase Hybrid Nanoflowers and Co-Immobilization on Resins for Improved Enzyme Activity, Stability, and Processability.

As a low-calorie sugar, D-allulose is produced from D-fructose catalyzed by D-allulose 3-epimerase (DAE). Here, to improve the catalytic activity, stability, and processability of DAE, we reported a novel method by forming organic-inorganic hybrid nanoflowers (NF-DAEs) and co-immobilizing them on resins to form composites (Re-NF-DAEs). NF-DAEs were prepared by combining DAE with metal ions (Co2+, Cu2+, Zn2+, Ca2+, Ni2+, Fe2+, and Fe3+) in PBS buffer, and were analyzed by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy, and X-ray diffraction. All of the NF-DAEs showed higher catalytic activities than free DAE, and the NF-DAE with Ni2+ (NF-DAE-Ni) reached the highest relative activity of 218%. The NF-DAEs improved the thermal stability of DAE, and the longest half-life reached 228 min for NF-DAE-Co compared with 105 min for the free DAE at 55 °C. To further improve the recycling performance of the NF-DAEs in practical applications, we combined resins and NF-DAEs to form Re-NF-DAEs. Resins and NF-DAEs co-effected the performance of the composites, and ReA (LXTE-606 neutral hydrophobic epoxy-based polypropylene macroreticular resins)-based composites (ReA-NF-DAEs) exhibited outstanding relative activities, thermal stabilities, storage stabilities, and processabilities. The ReA-NF-DAEs were able to be reused to catalyze the conversion from D-fructose to D-allulose, and kept more than 60% of their activities after eight cycles.

A High-Rate Hybrid BCI System Based on High-Frequency SSVEP and sEMG.

Recently, various biosignals have been combined with electroencephalography (EEG) to build hybrid brain-computer interface (BCI) systems to improve system performance. Since steady-state visual evoked potential (SSVEP) and surface electromyography (sEMG) are easy-to-use, non-invasive techniques, and have high signal-to-noise ratio (SNR), hybrid BCI systems combining SSVEP and sEMG have received much attention in the BCI literature. However, most existing studies regarding hybrid BCIs based on SSVEP and sEMG adopt low-frequency visual stimuli to induce SSVEPs. The comfort of these systems needs further improvement to meet the practical application requirements. The present study realized a novel hybrid BCI combining high-frequency SSVEP and sEMG signals for spelling applications. EEG and sEMG were obtained simultaneously from the scalp and skin surface of subjects, respectively. These two types of signals were analyzed independently and then combined to determine the target stimulus. Our online results demonstrated that the developed hybrid BCI yielded a mean accuracy of 88.07 ± 1.43% and ITR of 159.12 ± 4.31 bits/min. These results exhibited the feasibility and effectiveness of fusing high-frequency SSVEP and sEMG towards improving the total BCI system performance.

A Novel Hybrid Brain-Computer Interface Combining Motor Imagery and Intermodulation Steady-State Visual Evoked Potential.

The hybrid brain-computer interface (hBCI) combining motor imagery (MI) and steady-state visual evoked potential (SSVEP) has been proven to have better performance than a pure MI- or SSVEP-based brain-computer interface (BCI). In most studies on hBCIs, subjects have been required to focus their attention on flickering light-emitting diodes (LEDs) or blocks while imagining body movements. However, these two classical tasks performed concurrently have a poor correlation. Therefore, it is necessary to reduce the task complexity of such a system and improve its user-friendliness. Aiming to achieve this goal, this study proposes a novel hybrid BCI that combines MI and intermodulation SSVEPs. In the proposed system, images of both hands flicker at the same frequency (i.e., 30 Hz) but at different grasp frequencies (i.e., 1 Hz for the left hand, and 1.5 Hz for the right hand), resulting in different intermodulation frequencies for encoding targets. Additionally, movement observation for subjects can help to perform the MI task better. In this study, two types of brain signals are classified independently and then fused by a scoring mechanism based on the probability distribution of relevant parameters. The online verification results showed that the average accuracies of 12 healthy subjects and 11 stroke patients were 92.40 ± 7.45% and 73.07 ± 9.07%, respectively. The average accuracies of 10 healthy subjects in the MI, SSVEP, and hybrid tasks were 84.00 ± 12.81%, 80.75 ± 8.08%, and 89.00 ± 9.94%, respectively. The high recognition accuracy verifies the feasibility and robustness of the proposed system. This study provides a novel and natural paradigm for a hybrid BCI based on MI and SSVEP.