The neural oscillatory mechanism underlying human brain fingerprint recognition using a portable EEG acquisition device.

In recent years, brainprint recognition has emerged as a novel method of personal identity verification. Although studies have demonstrated the feasibility of this technology, some limitations hinder its further development into the society, such as insufficient efficiency (extended wear time for multi-channel EEG cap), complex experimental paradigms (more time in learning and completing experiments), and unclear neurobiological characteristics (lack of intuitive biomarkers and an inability to eliminate the impact of noise on individual differences). Overall, these limitations are due to the incomplete understanding of the underlying neural mechanisms. Therefore, this study aims to investigate the neural mechanisms behind brainwave recognition and simplify the operation process. We recorded prefrontal resting-state EEG data from 40 participants, which is followed up over nine months using a single-channel portable brainwave device. We found that portable devices can effectively and stably capture the characteristics of different subjects in the alpha band (8-13Hz) over long periods, as well as capturing their individual differences (no alpha peak, 1 alpha peak, or 2 alpha peaks). Through correlation analysis, alpha-band activity can reveal the uniqueness of the subjects compared to others within one minute. We further used a descriptive model to dissect the oscillatory and non-oscillatory components in the alpha band, demonstrating the different contributions of fine oscillatory features to individual differences (especially amplitude and bandwidth). Our study validated the feasibility of portable brainwave devices in brainwave recognition and the underlying neural oscillation mechanisms. The fine characteristics of various alpha oscillations will contribute to the accuracy of brainwave recognition, providing new insights for the development of future brainwave recognition technology.

Physiological system analysis of the kidney by high-temporal-resolution T2∗ monitoring of an oxygenation step response.

PURPOSE: Examine the feasibility of characterizing the regulation of renal oxygenation using high-temporal-resolution monitoring of the T2∗ response to a step-like oxygenation stimulus. METHODS: For T2∗ mapping, multi-echo gradient-echo imaging was used (temporal resolution = 9 seconds). A step-like renal oxygenation challenge was applied involving sequential exposure to hyperoxia (100% O2 ), hypoxia (10% O2 + 90% N2 ), and hyperoxia (100% O2 ). In vivo experiments were performed in healthy rats (N = 10) and in rats with bilateral ischemia-reperfusion injury (N = 4). To assess the step response of renal oxygenation, a second-order exponential model was used (model parameters: amplitude [A], time delay [Δt], damping constant [D], and period of the oscillation [T]) for renal cortex, outer stripe of the outer medulla, inner stripe of the outer medulla, and inner medulla. RESULTS: The second-order exponential model permitted us to model the exponential T2∗ recovery and the superimposed T2∗ oscillation following renal oxygenation stimulus. The in vivo experiments revealed a difference in Douter medulla between healthy controls (D < 1, indicating oscillatory recovery) and ischemia-reperfusion injury (D > 1, reflecting aperiodic recovery). The increase in Douter medulla by a factor of 3.7 (outer stripe of the outer medulla) and 10.0 (inner stripe of the outer medulla) suggests that this parameter might be rather sensitive to (patho)physiological oxygenation changes. CONCLUSION: This study demonstrates the feasibility of monitoring the dynamic oxygenation response of renal tissues to a step-like oxygenation challenge using high-temporal-resolution T2∗ mapping. Our results suggest that the implemented system analysis approach may help to unlock questions regarding regulation of renal oxygenation, with the ultimate goal of providing imaging means for diagnostics and therapy of renal diseases.

[Succinic acid production from sucrose and sugarcane molasses by metabolically engineered Escherichia coli].

Sugarcane molasses containing large amounts of sucrose is an economical substrate for succinic acid production. However, Escherichia coli AFP111 cannot metabolize sucrose although it is a promising candidate for succinic acid production. To achieve sucrose utilizing ability, we cloned and expressed cscBKA genes encoding sucrose permease, fructokinase and invertase of non-PTS sucrose-utilization system from E. coli W in E. coli AFP111 to generate a recombinant strain AFP111/pMD19T-cscBKA. After 72 h of anaerobic fermentation of the recombinant in serum bottles, 20 g/L sucrose was consumed and 12 g/L succinic acid was produced. During dual-phase fermentation comprised of initial aerobic growth phase followed by anaerobic fermentation phase, the concentration of succinic acid from sucrose and sugarcane molasses was 34 g/L and 30 g/L, respectively, at 30 h of anaerobic phase in a 3 L fermentor. The results show that the introduction of non-PTS sucrose-utilization system has sucrose-metabolizing capability for cell growth and succinic acid production, and can use cheap sugarcane molasses to produce succinic acid.

[Effects of chemically modified sugarcane bagasse on butanol production by immobilized Clostridium acetobutylicum XY16].

Sugarcane bagasse modified by polyethylenimine (PEI) and glutaraldehyde (GA) was used as a carrier to immobilize Clostridium acetobutylicum XY16 in the process of butanol production. The effects of chemically modified sugarcane bagasse on batch and repeat-batch fermentations were investigated. Batch fermentation was conducted with an addition of 10 g/L modified sugarcane bagasse and 60 g/L glucose, resulting in a high solvent concentration of 21.67 g/L and productivity of 0.60 g/(L x h) with the treatment of 4 g/L PEI and 1 g/L GA. Compared to the fermentations by free cells and immobilized cells on unmodified sugarcane bagasse, the productivity increased 130.8% and 66.7%, respectively. The fibrous-bed bioreactor also maintained a stable butanol production during repeat-batch fermentations, achieving a maximum productivity of 0.83 g/(L x h) with a high yield of 0.42 g/g.

[A fast approach for level set segmentation].

To propose an optimal level set approach for fast medical image segmentation. By confining the computation quantity of the level sets function and using the image characteristics, we improved the efficiency of segmentation and decreased the parameter setting in some degree for DSA vascular segmentation.