Baicalin protects neonatal rat brains against hypoxicischemic injury by upregulating glutamate transporter 1 via the phosphoinositide 3-kinase/protein kinase B signaling pathway

Baicalin is a flavonoid compound extracted from Scutellaria baicalensis root.Recent evidence indicates that baicalin is neuroprotective in models of ischemic stroke.Here,we investigate the neuroprotective effect of baicalin in a neonatal rat model of hypoxic-ischemic encephalopathy.Seven-day-old pups underwent left common carotid artery ligation followed by hypoxia（8% oxygen at 37°C） for 2 hours,before being injected with baicalin（120 mg/kg intraperitoneally） and examined 24 hours later.Baicalin effectively reduced cerebral infarct volume and neuronal loss,inhibited apoptosis,and upregulated the expression of p-Akt and glutamate transporter 1.Intracerebroventricular injection of the phosphoinositide 3-kinase/protein kinase B（PI3 K/Akt） inhibitor LY294002 30 minutes before injury blocked the effect of baicalin on p-Akt and glutamate transporter 1,and weakened the associated neuroprotective effect.Our findings provide the first evidence,to our knowledge that baicalin can protect neonatal rat brains against hypoxic-ischemic injury by upregulating glutamate transporter 1 via the PI3 K/Akt signaling pathway.

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.

Plant Sensing and Signaling in Response to K＋-Deficiency

Potassium （K＋） is one of the essential macronutrients for plant growth and development. However, K＋ content in soils is usually limited so that the crop yields are restricted. Plants may adapt to K＋-deficient environment by adjusting their physiological and morphological status, indicating that plants may have evolved their sensing and signaling mechanisms in response to K＋-deficiency. This short review particularly discusses some components as possible sensors or signal transducers involved in plant sensing and signaling in response to K＋-deficiency, such as K＋ channels and transporters, H＋-ATPase, some cytoplasmic enzymes, etc. Possible involvement of Ca2＋ and ROS signals in plant responses to K＋-deficiency is also discussed.

Molecular rotors as a class of generally highly active ion transporters

We describe here a class of unconventional ion transporters,molecular rotors that transport ions through a rotating function rather than via traditional carrier or channel mechanisms.Mimicking macroscopic rotors,these molecular rotors consist of three modularly tunable components,i.e.,a membrane-anchoring stator,a crown ether-containing rotator for ion binding and transport,and a triple bond-based axle that allows the rotator to freely rotate around the stator in the lipid membrane.Lipid bilayer experiments reveal the generally high ability of all molecular rotors in promoting the highly efficient transmembrane K^(+)flux(EC50 values=0.49-1.37 mol%relative to lipid).While molecular rotors differing only in the ion-binding unit exhibit similar ion transport activities,those differing in the rotator’s length display activity differences by up to 174%.