Enhanced acidic resistance ability and catalytic properties of Bacillus 1,3-1,4-ß-glucanases by sequence alignment and surface charge engineering.

High stability at acidic environment is required for 1,3-1,4-ß-glucanase to function in biofuel, brewing and animal feed industries. In this study, a mesophilic ß-glucanase from Bacillus terquilensis CGX 5-1 was rationally engineered through sequence alignment and surface charge engineering to improve its acidic resistance ability. Nineteen singly-site variants were constructed and Q1E, I133L and V134A variants showed better acidic stability without the compromise of catalytic property and thermostability. Furthermore, four multi-site variants were constructed and one double-site variant Q1E/I133L with better stability at acidic environment and higher catalytic property was obtained. The fluorescence spectroscopy and structural analysis showed that more surface negative charge, decreased exposure degree of residue No.1, shifted side chain direction of residue No.133 and the lower total and folding free energy might be the reason for the improvement of acidic stability of Q1E/I133L variant. The obtained Q1E/I133L variant has potential applications in industries.

Development and validation of an UPLC-PDA method for the determination of corilagin in rat plasma and its application to pharmacokinetic study.

Corilagin, which was isolated from several medical herbs, has been reported to exert many pharmacological activities. A simple and rapid liquid ultra-performance liquid chromatography (UPLC) coupled to photodiode array (PDA) method has been developed to quantify corilagin in rat plasma. In this study, plasma samples were prepared by ethyl acetate extraction. Separation was performed on a HSS T3 (100mm×2.1mm, 1.8µm) column by using a mobile phase of acetonitrile and water with 0.1% trifluoroacetic acid (v/v). Corilagin and internal standard epicatechin were detected at a wavelength of 266nm. The calibration curve was linear (r>0.998) over a concentration range of 0.2µg/mL to 20µg/mL with a lower quantification limit of 0.2µg/mL. Both intra and inter-day precision values were within 5.7% and extraction recovery were greater than 81.0%. Stability tests showed that corilagin and IS remained stable during the analytical procedure. The validated UPLC-PDA method was then used to analyze the pharmacokinetics of corilagin administered to rats intravenously (10mg/kg) or orally (50mg/kg). Oral bioavailability of corilagin was calculated to be 10.7%, indicating that this component is not suitable for oral administration. The results provide basis for further preclinical studies on corilagin.

Convergence analysis of online gradient method for BP neural networks.

This paper considers a class of online gradient learning methods for backpropagation (BP) neural networks with a single hidden layer. We assume that in each training cycle, each sample in the training set is supplied in a stochastic order to the network exactly once. It is interesting that these stochastic learning methods can be shown to be deterministically convergent. This paper presents some weak and strong convergence results for the learning methods, indicating that the gradient of the error function goes to zero and the weight sequence goes to a fixed point, respectively. The conditions on the activation function and the learning rate to guarantee the convergence are relaxed compared with the existing results. Our convergence results are valid for not only S-S type neural networks (both the output and hidden neurons are Sigmoid functions), but also for P-P, P-S and S-P type neural networks, where S and P represent Sigmoid and polynomial functions, respectively.

Deterministic convergence of an online gradient method for BP neural networks.

Online gradient methods are widely used for training feedforward neural networks. We prove in this paper a convergence theorem for an online gradient method with variable step size for backward propagation (BP) neural networks with a hidden layer. Unlike most of the convergence results that are of probabilistic and nonmonotone nature, the convergence result that we establish here has a deterministic and monotone nature.