Harmine Ameliorates Cognitive Impairment by Inhibiting NLRP3 Inflammasome Activation and Enhancing the BDNF/TrkB Signaling Pathway in STZ-Induced Diabetic Rats

Diabetes mellitus (DM) is considered a risk factor for cognitive dysfunction. Harmine not only effectively improves the symptoms of DM but also provides neuroprotective effects in central nervous system diseases. However, whether harmine has an effect on diabetes-induced cognitive dysfunction and the underlying mechanisms remain unknown. In this study, the learning and memory abilities of rats were evaluated by the Morris water maze test. Changes in the nucleotide-binding oligomerization domain-containing protein (NOD)-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome and brain-derived neurotrophic factor (BDNF)/TrkB signaling pathway were determined in both streptozotocin (STZ)-induced diabetic rats and high glucose (HG)-treated SH-SY5Y cells by western blotting and histochemistry. Herein, we found that harmine administration significantly ameliorated learning and memory impairment in diabetic rats. Further study showed that harmine inhibited NLRP3 inflammasome activation, as demonstrated by reduced NLRP3, ASC, cleaved caspase-1, IL-1β, and IL-18 levels, in the cortex of harmine-treated rats with DM. Harmine was observed to have similar beneficial effects in HG-treated neuronal cells. Moreover, we found that harmine treatment enhanced BDNF and phosphorylated TrkB levels in both the cortex of STZ-induced diabetic rats and HG-treated cells. These data indicate that harmine mitigates cognitive impairment by inhibiting NLRP3 inflammasome activation and enhancing the BDNF/TrkB signaling pathway. Thus, our findings suggest that harmine is a potential therapeutic drug for diabetes-induced cognitive dysfunction

Fabrication and Characterization of Porous Core–Shell Graphene/SiO2 Nanocomposites for the Removal of Cationic Neutral Red Dye

Porous rGO/SiO2 nanocomposites with a “core-shell” structure were prepared as an efficient adsorbent for the liquid-phase adsorption of cationic neutral red (NR) dye. The samples were characterized with powder X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), thermogravimetric analysis (TG), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and N2 and water vapor adsorption/desorption methods. The NR removal ability and kinetics of the adsorption process of SiO2 and the rGO/SiO2 nanocomposites were investigated at 298 K. The rGO/SiO2 nanocomposite SG 0.30 showed a superior adsorption of NR dye. In regard to NR at pH 5, we measured a superior adsorption capacity of 66.635 mg/g at an initial NR concentration of 50 mg/L. The experimental adsorption capacity of SG 0.30 was 3.791 times higher than that of SiO2. Then, we compared the results with similar materials used for NR removal. Moreover, the water adsorption sites provided by the nitrogen- and oxygen-containing groups might be one of the reasons for the increased adsorption of water vapor. The broad range of properties of the rGO/SiO2 nanocomposite, including its simple synthesis, ability to be mass prepared, and strong adsorption properties, makes it a truly novel adsorbent that can be industrially produced, and shows potential application in the treatment of wastewater-containing dyes

Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network

The traditional ampere-hour (Ah) integration method ignores the influence of battery health (SOH) and considers that the battery capacity will not change over time. To solve the above problem, we proposed a joint SOH-SOC estimation model based on the GWO-BP neural network to optimize the Ah integration method. The method completed SOH estimation through the GWO-BP neural network and introduced SOH into the Ah integration method to correct battery capacity and improve the accuracy of state of charge (SOC) estimation. In addition, the method also predicted the SOH of the battery, so the driver could have a clearer understanding of the battery aging level. In this paper, the stability of the joint SOH-SOC estimation model was verified by using different battery data from different sources. Comparative experimental results showed that the estimation error of the joint SOH-SOC estimation model could be stabilized within 5%, which was smaller compared with the traditional ampere integration method

Study of SOC Estimation by the Ampere-Hour Integral Method with Capacity Correction Based on LSTM

The estimation of the state of charge (SOC) of a battery’s power is one of the key technologies in a battery management system (BMS). As a common SOC estimation method, the traditional ampere-hour integral method regards the actual capacity of the battery, which is constantly changed by the usage conditions and environment, as a constant for calculation, which may cause errors in the results of SOC estimation. Considering the above problems, this paper proposes an improved ampere-hour integral method based on the Long Short-Term Memory (LSTM) network model. The LSTM network model is used to obtain the actual battery capacity variation, replacing the fixed value of battery capacity in the traditional ampere-hour integral method and optimizing the traditional ampere-hour integral method to improve the accuracy of the SOC estimation method. The experimental results show that the errors of the results obtained by the improved ampere-hour integral method for the SOC estimation are all less than 10%, which proves that the proposed design method is feasible and effective

g-C3N4-triggered super synergy between photocatalysis and ozonation attributed to promoted (OH)-O-center dot generation

We coupled g-C3N4 or chlorine modified g-C3N4 (Cl/g-C3N4) photocatalysis with ozonation for mineralization of oxalic acid (OA) under visible light. g-C3N4 and Cl/g-C3N4 could trigger a super synergy between photocatalysis and ozonation, with a coupling coefficient at 17.8 and 9.9, respectively. The great gap of redox potential between the conduction band of g-C3N4 and ozone greatly benefitted electrons captured by ozone molecules, and thus promoted charge separation and ozone self-decomposition into a growing number of hydroxyl radicals in a photocatalytic ozonation process. Besides, the influence of chlorine modification on g-C3N4 to photocatalysis and photocatalytic ozonation was also clearly stated. (C) 2015 Elsevier B.V. All rights reserved