Modulation Steering Motion by Quantitative Electrical Stimulation in Pigeon Robots.

The pigeon robot has attracted significant attention in the field of animal robotics thanks to its outstanding mobility and adaptive capability in complex environments. However, research on pigeon robots is currently facing bottlenecks, and achieving fine control over the motion behavior of pigeon robots through brain-machine interfaces remains challenging. Here, we systematically quantify the relationship between electrical stimulation and stimulus-induced motion behaviors, and provide an analytical method to demonstrate the effectiveness of pigeon robots based on electrical stimulation. In this study, we investigated the influence of gradient voltage intensity (1.2-3.0 V) on the indoor steering motion control of pigeon robots. Additionally, we discussed the response time of electrical stimulation and the effective period of the brain-machine interface. The results indicate that pigeon robots typically exhibit noticeable behavioral responses at a 2.0 V voltage stimulus. Increasing the stimulation intensity significantly controls the steering angle and turning radius (p < 0.05), enabling precise control of pigeon robot steering motion through stimulation intensity regulation. When the threshold voltage is reached, the average response time of a pigeon robot to the electrical stimulation is 220 ms. This study quantifies the role of each stimulation parameter in controlling pigeon robot steering behavior, providing valuable reference information for the precise steering control of pigeon robots. Based on these findings, we offer a solution for achieving precise control of pigeon robot steering motion and contribute to solving the problem of encoding complex trajectory motion in pigeon robots.

Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques.

Continuous noninvasive blood glucose monitoring and estimation management by using photoplethysmography (PPG) technology always have a series of problems, such as substantial time variability, inaccuracy, and complex nonlinearity. This paper proposes a blood glucose (BG) prediction model for more precise prediction based on BG series decomposition by complete aggregation empirical mode decomposition based on adaptive white noise (CEEMDAN) and the gated recurrent unit (GRU) that is optimized by improved bacterial foraging optimization (IBFO). Hierarchical clustering technology recombines the decomposed BG series according to their sample entropy and the correlations with the original BG trends. Dynamic BG trends are regressed separately for each recombined BG series by the GRU model to realize the more precise estimations, which are optimized by IBFO for its structure and superparameters. Through experiments, the optimized and basic LSTM, RNN, and support vector regression (SVR) are compared to evaluate the performance of the proposed model. The experimental results indicate that the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the 15-min IBFO-GRU prediction is improved on average by about 13.1% and 18.4%, respectively, compared with those of the RNN and LSTM optimized by IBFO. Meanwhile, the proposed model improved the Clarke error grid results by about 2.6% and 5.0% compared with those of the IBFO-LSTM and IBFO-RNN in 30-min prediction and by 4.1% and 6.6% in 15-min ahead forecast, respectively. The evaluation outcomes of our proposed CEEMDAN-IBFO-GRU model have high accuracy and adaptability and can effectively provide early intervention control of the occurrence of hyperglycemic complications.

Continual and accurate home monitoring of uric acid in urine samples with uricase-packaged nanoflowers assisted portable electrochemical uricometer.

A convenient, fast and non-invasive portable electrochemical uricometer (PUM) assisted with the uricase-packaged nanoflowers (NFs) was constructed for continually and accurately monitoring of uric acid (UA) in urine samples at random intervals in just 20 s. Only a small amount of urine (50 µL) was needed for each test. Electrochemical deposition was adopted to modify gold nanoparticles (AuNPs) on screen-printed carbon electrodes (SPCE) and uricase-inorganic hybrid NFs (UOx-NFs) induced by calcium ions (Ca2+) were introduced for UA detection with expected specificity. Cyclic voltammetry (CV) (detection limit of 8.87 µM and liner range of 0-4 mM) and amperometry (detection limit of 0.82 µM and liner range of 0-5 mM) protocols were studied for UA detection, respectively. Finally, the uric acid in urine had be successfully continually monitored from volunteers with various dietary choosing, the results of which can be adopted as the effective evidence for uric acid control.

Aramid nanofiber/bacterial cellulose composite separators for lithium-ion batteries.

Aramid nanofibers (ANFs) are expected to serve as new nanoscale building blocks which could improve both the mechanical performance and ionic conductivity of bacterial cellulose (BC) membranes due to their high interfacial bonding, matching of fiber size and the polar aramid groups in ANFs. Herein, composite membranes of BC and ANFs with different ANFs loadings of 2%, 4%, 6% and 8% were prepared by a traditional paper-making method after homogeneous mixing. The achieved membranes with relatively low ANFs loadings exhibited the increased tensile strength and ionic conductivity. The Li/LiFePO4 half-cells based on the 2%ANFs/BC separator demonstrated the best electrochemical performance (including discharge capacity, C-rate capability and cycling stability). Thus, the proposed ANFs/BC separators are a promising candidate for high-performance Li-ion batteries.

A hybrid system integrating xylose dehydrogenase and NAD+ coupled with PtNPs@MWCNTs composite for the real-time biosensing of xylose.

The wide application of xylose in the food, beverage, and pharmaceutical industries, as well as in the booming field of biorefinery, raises the demand for a rapid, accurate, and real-time xylose-sensing technique to rival the conventional methods based on chromatography, spectroscopy, and electrochemical analysis using non-specific enzymes or abiotic catalysts. Herein, a hybrid system comprising polyethylene glycerol swing-arm-tethered NAD+ and xylose dehydrogenase (XDH), coupled with platinum nanoparticles deposited on carbon nanotubes (PtNPs@MWCNTs), was constructed for the real-time sensing of xylose. The use of the PtNPs@MWCNTs composite enhanced the sensitivity of the electric response and reduced the oxidation potential of NADH significantly. Further, the NAD+ immobilization allowed an increase in its microenvironment concentration and facilitated cofactor regeneration. The screen-printed electrode cast with the hybrid system showed a wide xylose detection range of 0.5 to 10 mM or 3.33 to 66.61 mM, and a low detection limit of 0.01 mM or 3.33 mM (S/N = 3), when connected to a potentiostat or a homemade portable biosensor, respectively. The biosensor also exhibited excellent working stability as it retained 82% of its initial performance after 30 days. The analysis of various xylose-containing samples further revealed the merits of our portable xylose biosensor in real-time sensing, including its rapid response, inexpensive instrumentation, and high selectivity, suggesting its great potential in practical applications.

Ratiometric electrochemical sensor for accurate detection of salicylic acid in leaves of living plants.

Detection of signal molecules in living plants is of great relevance for precision farming. In this work, to establish a more effective method for monitoring salicylic acid (SA) in the leaves of living plants, a ratiometric electrochemical sensor was fabricated based on a Cu metal-organic framework (Cu-MOF) and carbon black (CB) composite. The Cu-MOF and CB composite was used to catalyze SA oxidation. Ratiometric oxidation current peak intensities I SA/I Cu-MOFs were used as the response signal for SA. I SA/I Cu-MOFs linearly enhanced with the increase of SA concentration, together with low limits of detection (12.50 µM). Moreover, our sensor is fabricated on a screen-printed electrode (SPE), which is especially suitable for applying to the flat leaves of plants. Using this sensor, the SA level in the leaves of cucumber seedlings was monitored in vivo under salt stress. The proposed sensor is accurate, reliable and practical. This is the first report for developing a ratiometric electrochemical sensor for detecting SA in living plants. Our work can also provide a strategy for in vivo studies on the leaves of plants.

Spike and Local Field Synchronization Between the Pedunculopontine Nucleus and Primary Motor Cortex in a Rat Model of Parkinson's Disease.

The pedunculopontine nucleus (PPN) shows altered electrophysiological and anatomic characteristics in Parkinson's disease (PD), but little is known about the effect of 6-hydroxydopamine (6-OHDA) lesion and levodopa (L-DOPA) therapy on the relationship between spike and local field potential (LFP) activities in the PPN and motor cortex. Aiming to investigate this, synchronous spike and LFP signals in the PPN and primary motor cortex (M1) were recorded. The spike-LFP relationship was evaluated using coherence analysis, phase-lock and spike-field coherence (SFC). The results suggested that 6-OHDA lesion had a significant effect on the spike-LFP relationship between the PPN and M1 in rats under a rest or locomotion state. The significantly altered frequency bands varied across different neuron types and animal activity states. In addition, the altered coherence values between PPN spike and M1 LFP were refractory to long-term L-DOPA therapy although all other changes could be reversed by this drug treatment. All results provided evidence of the spike-LFP relationship between the PPN and M1 in PD, revealing some network mechanisms of the cortico-basal ganglia circuitry and PPN, which might be an underlying candidate for PD pathophysiology and therapy.

A facile synthesis of perforated reduced graphene oxide for high performance electrochemical sensors.

Highly active perforated reduced graphene oxide (P-rGO) was synthesized by a facile methodology based on co-deposition of graphene oxide with sacrificial Prussian blue. Electrode surface properties were characterized by SEM and EDS. The GC/P-rGO electrode exhibited a larger specific surface area than that of GCE. These findings highlighted that the signal was enhanced for both dopamine detection and selenium detection by using P-rGO as a relevant supporting substrate. The result indicated that the large number of perforated structures formed numerous electrically conductive channels in the structure, improving the electrocatalytic properties.