Edible fungus is widely eaten because of their delicious taste and high nutritional value. Growing evidence indicates that edible fungus is rich in various active ingredients including polysaccharides, proteins, fats, vitamins, and other ingredients. Polysaccharides are one of the main active ingredients of edible fungi. Increasing research has confirmed that edible fungus polysaccharides (EFPs) show multiple biological activities such as antioxidant, anti-inflammatory, anti-tumor, immune regulation, anti-virus, gut microbiota regulation, hypoglycemic, etc. Hence, EFP related research has received more and more attention due to their non-toxic, high medical, and nutritional values. This paper reviews the latest research progress of EFPs biological activities, and looks forward to their future development trend. This review provides a theoretical basis for studying the application of EFPs in the fields of biomedicine, cosmetics, health food, and promotes the development and utilization of EFPs.
The uncertainty of noise statistics in dynamic systems is one of the most important issues in engineering applications, and significantly affects the performance of state estimation. The optimal Bayesian Kalman filter (OBKF) is an important approach to solve this problem, as it is optimal over the posterior distribution of unknown noise parameters. However, it is not suitable for online estimation because the posterior distribution of unknown noise parameters at each time is derived from its prior distribution by incorporating the whole measurement sequence, which is computationally expensive. Additionally, when the system is subjected to large disturbances, its response is slow and the estimation accuracy deteriorates. To solve the problem, we improve the OBKF mainly in two aspects. The first is the calculation of the posterior distribution of unknown noise parameters. We derive it from the posterior distribution at a previous time rather than the prior distribution at the initial time. Instead of the whole measurement sequence, only the nearest fixed number of measurements are used to update the posterior distribution of unknown noise parameters. Using the sliding window technique reduces the computational complexity of the OBKF and enhances its robustness to jump noise. The second aspect is the estimation of unknown noise parameters. The posterior distribution of an unknown noise parameter is represented by a large number of samples by the Markov chain Monte Carlo approach. In the OBKF, all samples are equivalent and the noise parameter is estimated by averaging the samples. In our approach, the weights of samples, which are proportional to their likelihood function values, are taken into account to improve the estimation accuracy of the noise parameter. Finally, simulation results show the effectiveness of the proposed method.
The classical Kalman filter is a very important state estimation approach, which has been widely used in many engineering applications. The Kalman filter is optimal for linear dynamic systems with independent Gaussian noises. However, the independence and Gaussian assumptions may not be satisfied in practice. On the one hand, modeling physical systems usually results in discrete-time state-space models with correlated process and measurement noises. On the other hand, the noise is non-Gaussian when the system is disturbed by heavy-tailed noise. In this case, the performance of the Kalman filter will deteriorate, or even diverge. This paper is devoted to addressing the state estimation problem of linear dynamic systems with high-order autoregressive moving average (ARMA) non-Gaussian noise. First, a triplet Markov model is introduced to model the system with high-order ARMA noise, since this model relaxes the independence assumption of the hidden Markov model. Then, a new filter is derived based on correntropy, instead of the commonly used minimum mean square error (MMSE), to deal with non-Gaussian noise. Unlike the MMSE, which uses only second-order statistics of error, correntropy can capture second-order and higher-order statistics. Finally, simulation results verify the effectiveness of the proposed algorithm.
Hawthorn (Crataegus pinnatifida Bge.) contains various active components including polysaccharides, polyphenols, vitamin, phenolic acid etc. Polysaccharides are one of the most critical active components in hawthorn, which exhibits different biological activities such as immunomodulatory antioxidant, anti‐inflammatory, hypoglycemic, and other activities. Hence, the main purpose of this paper is optimize the aqueous two‐phase extraction (UTPE) polysaccharides from hawthorn via response surface methodology coupled genetic algorithm (RSM‐GA) and then evaluated its antioxidant activity through free radicals scavenging experiments. The results show that the optimal extraction conditions to achieve the maximum polysaccharides yield (6.42±0.08)% from hawthorn by UTPE was obtained under the mass fraction of ammonium sulfate of 11%, extraction temperature of 57 °C, liquid‐to‐solid ratio of 33 mL/g, ethanol concentration of 26%, and extraction time of 30 min, and the relative error between the experimental value and the theoretical value was 4.56%. The antioxidant capacity enhanced with the increase of hawthorn polysaccharides (HPs) concentration. However, the antioxidant activity of HPs was weaker than that of ascorbic acid. The results of this study provide a critical material basis for promoting the further research of HPs, and have certain significance for the deep processing and product development of hawthorn.This article is protected by copyright. All rights reserved
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