Rapid screening, isolation, and activity evaluation of potential xanthine oxidase inhibitors in Polyporus umbellatus and mechanism of action in the treatment of gout.

INTRODUCTION: Studies show that Polyporus umbellatus has some pharmacological effects in enhancing immunity and against gout. OBJECTIVES: We aimed to establish new techniques for extraction, biological activity screening, and preparation of xanthine oxidase inhibitors (XODIs) from P. umbellatus. METHODS: First, the extraction of P. umbellatus was investigated using the back propagation (BP) neural network genetic algorithm mathematical regression model, and the extraction variables were optimised to maximise P. umbellatus yield. Second, XODIs were rapidly screened using ultrafiltration, and the change of XOD activity was tested by enzymatic reaction kinetics experiment to reflect the inhibitory effect of active compounds on XOD. Meanwhile, the potential anti-gout effects of the obtained active substances were verified using molecular docking, molecular dynamics simulations, and network pharmacology analysis. Finally, with activity screening as guide, a high-speed countercurrent chromatography (HSCCC) method combined with consecutive injection and two-phase solvent system preparation using the UNIFAC mathematical model was successfully developed for separation and purification of XODIs, and the XODIs were identified using MS and NMR. RESULTS: The results verified that polyporusterone A, polyporusterone B, ergosta-4,6,8(14),22-tetraen-3-one, and ergosta-7,22-dien-3-one of P. umbellatus exhibited high biological affinity towards XOD. Their structures have been further identified by NMR, indicating that the method is effective and applicable for rapid screening and identification of XODIs. CONCLUSION: This study provides new ideas for the search for natural XODIs active ingredients, and the study provide valuable support for the further development of functional foods with potential therapeutic benefits.

Influencing factors of selenium transformation in a soil-rice system and prediction of selenium content in rice seeds: a case study in Ninghua County, Fujian Province.

Selenium (Se) is an essential element for human and animal health and has antioxidant, anticancer, and antiviral effects. However, more than 100 million people in China do not have enough Se in their diets, resulting in a state of low Se in the human body. Since the absorption of Se by crop seeds depends not only on the Se content in soil, there are many omissions and misjudgments in the division of Se-rich producing areas. Soil pH, total iron oxide content (TFe2O3), soil organic matter (SOM), and P and S contents were the main factors affecting Se migration and transformation in the soil-rice system. In this study, we compared the performance of the back propagation neural network (BP network) and multiple linear regression (MLR) using 177 pairs of soil-rice samples. Our results showed that the BP network had higher accuracy than MLR. The accuracy and precision of the prediction data met the requirements, and the prediction data were reliable. Based on the Se data of surface paddy fields, 26,900 ha of Se-rich rice planting area was planned using this model, accounting for 77% of the paddy field area. In the planned Se-rich area for rice, the proportion of soil Se content greater than 0.4 mg·kg-1 was only 5.29%. Our research is of great significance for the development of Se-rich lands.

Grade evaluation of black-odorous urban rivers in the Greater Bay Area of China using an improved back propagation (BP) neural network.

With the rapid development of urbanization, the urban water environment is receiving continuous attention. It is necessary to understand water quality in a timely manner and make a reasonable comprehensive evaluation. However, existing black-odorous water grade evaluation guidelines are not sufficient. Understanding the changing situation of black-odorous water in urban rivers is a growing concern, especially in real-world scenarios. In this study, a BP neural network combined with the fuzzy membership degree was applied to evaluate the black-odorous grade of urban rivers in Foshan City, which is within the Greater Bay Area of China. The optimal 4 × 11 × 1 topology structure of the BP model was constructed by taking the dissolved oxygen (DO), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), and total phosphorus (TP) concentrations as input water quality indicators. There was almost no occurrence of black-odorous water in the two public rivers outside the region in 2021. Black-odorous water was most significant in 10 urban rivers, with grade IV and grade V occurring over 50% of the time in 2021. These rivers had three features, i.e., parallel with a public river, beheaded, and close proximity to Guangzhou City, the provincial capital of Guangdong. The results of the grade evaluation of the black-odorous water found basically matched those of the water quality assessment. The existence of some inconsistencies between the two systems justified the necessity to expand and extend the number of employed indicators and grades in the present guidelines. The results confirm the capability of the BP neural network combined with the fuzzy-based membership degree in the quantitative grade evaluation of black-odorous water in urban rivers. This study makes a step forward in understanding the grading of black-odorous urban rivers. The findings can provide a reference for local policy-makers regarding the priority of practical engineering projects in prevailing water environment treatment programs.

Selection of optimal models for predicting growth stress in Artemisia desertorum by comparison of linear regression and multiple neural networks: Take the construction of a green mine in the Bayan Obo mine as an example.

In recent years, more and more countries are focusing on the control of mining sites and the surrounding ecological environment, and the new environmental concept of green mines has been proposed. By investigating the ecological background of a mine site, pollution and ecological imbalances in the mine can be predicted, managed or transformed. This study investigated the effects of rare earth elements on plant growth in the Baotou Bayan Obo Rare Earth Mine and evaluated soil contamination and subsequent remediation through the measured plant height. Using linear regression, BP(Back Propagation) neural networks, GA-BP(Genetic Algorithm- Back Propagation) neural networks, ELM(Extreme Learning Machine) and GA-ELM(Genetic Algorithm- Extreme Learning Machine) model prediction instruments, the different rare earth solution concentrations were set as input values and the heights of Artemisia desertorum, which as the model plant, were set as output values in the prediction. The results showed that the linear regression predicted the standard error of single La(III), Ce(III) solution and compound La(III) + Ce(III) solution for Artemisia desertorum growth stress was on the high side, 7.02%- 8.92%; the efficiency range of each group of models under BP neural network, GA-BP neural network and ELM neural network were 1.15%- 2.53%, 0.85%- 1.28%, 1.76%- 3.53%; while the efficiency range under GA-ELM neural network was 0.59%- 0.68%, with average error values and predicted values close to the true values. Among them, the MAPE of GA-ELM neural network are significantly lower than other models, and the error decreases with increasing concentration of the compound solution. So GA-ELM neural network can be used as an efficient, fast and reasonable optimal model for predicting the growth stress of Artemisia desertorum in Bayan Obo mining area. The experimental results can provide a theoretical basis for assessing the risk of soil rare earth contamination in the area, evaluating the expectation of later remediation, and provide a degree of new ideas for the construction of green mines.

Optimization of extraction and bioactivity detection of celery leaf flavonoids using BP neural network combined with genetic algorithm and response.

In the present study, ultrasound-assisted extraction was employed to extract the general flavone from celery leaves using response surface methodology and BP neural network model with a genetic algorithm (GA). The effects of temperature, time, solid-liquid ratio, and ethanol concentration on the extraction results were assessed by Box-Behnken design. Further optimization of the process was performed by GA-BP. Our results showed that the optimal conditions were an ethanol concentration of 70.31%, a temperature of 67.2 °C and an extraction time of 26.6 min. In addition, significant antioxidant activity and in vitro bacteriostasis were observed. We found that the total flavonoids of the celery leaves exerted a strong inhibitory effect on Escherichia coli, Staphylococcus aureus, and Bacillus subtilis. Additionally, considerable DPPH· and ·OH scavenging effects were exerted by flavonoids. Therefore, flavonoids from celery leaves can be considered natural antioxidants and bacterial inhibitors.

Using UAV image data to monitor the effects of different nitrogen application rates on tea quality.

BACKGROUND: Accurate and efficient evaluation of the effect of nitrogen application rate on tea quality is of great significance for nitrogen management in a tea garden. However, previous methods were all through soil or leaf sampling, using biochemical methods for laboratory testing. These methods are not only less one-time detection samples, but also time-consuming, laborious and inefficient. Therefore, the development of fast, efficient and non-destructive diagnostic methods is an important goal in this field. RESULTS: We obtained spectral information on the tea canopy using a multispectral camera carried by an unmanned aerial vehicle (UAV), and extracted the average DN value of the experimental plot by environmental visual imagery (ENVI); we finally obtained 28 spectral parameters. By analyzing the correlation between spectral parameters and ground parameters measured synchronously, five spectral parameters with high correlation were selected. Finally, the prediction models of tea nitrogen, polyphenol and amino acid content were established by using support vector machine (SVM), partial least squares and backpropagation neural network. Through modeling comparison and coefficient verification, the results show that the ground parameters measured in the laboratory were in good agreement with the results estimated by the model. The SVM model had the best performance in predicting nitrogen and tea polyphenol content, with R2  = 0.7583 and 0.7533, root mean square error of prediction (RMSEP) = 0.4086 and 0.3392, and normalized RMSEP (NRMSEP) = 1.23 and 1.28, respectively. The partial least squares regression model had the best performance in predicting amino acid content, with R2  = 0.7597, RMSEP = 0.1176 and NRMSEP = 4.10. CONCLUSION: The results show that the model based on UAV image data and machine learning algorithm can effectively detect the main biochemical components of the tea plant, which provides an important basis for tea garden management. © 2021 Society of Chemical Industry.

Research on the influence of attention and emotion of tea drinkers based on artificial neural network.

Tea can help to regulate the mood of human. Based on the influence of tea on people's mood and attention, this study explored the tea concentration when the mood and attention of drinkers are in the best state, and established the best concentration model of tea. Using sampling experiment method to collect objective data, which are then combined with questionnaire survey method to collect subjective data, using the results to establish a neural network algorithm model to test the accuracy of the neural network algorithm model. Experiments show that the correlation coefficient of the output value of the BP neural network model constructed in this study is basically consistent with the actual prediction result. After obtaining data such as age, gender, frequency of tea drinking, and tea drinking concentration of tea drinkers, the constructed back propagation (BP) neural network model can accurately predict the mental state score of tea drinkers. The research will provide certain data support and theoretical basis for the follow-up development of the tea industry. Follow-up work needs to be performed in order to further adjust the scope and accuracy of the control model. Then, a more complete and accurate advanced BP neural network model can be established for different types of tea and other parameters.

[Identification of curcumin content in raw and vinegar-processed rhizomes of Curcuma kwangsiensis based on electronic nose combined with back propagation neural network].

This study aimed to establish a rapid and accurate method for identification of raw and vinegar-processed rhizomes of Curcuma kwangsiensis, in order to predict the content of curcumin compounds for scientific evaluation. A complete set of bionics recognition mode was adopted. The digital odor signal of raw and vinegar-processed rhizomes of Curcuma kwangsiensis were obtained by e-nose, and analyzed by back propagation(BP) neural network algorithm, with the accuracy, the sensitivity and specificity in discriminant model, correlation coefficient as well as the mean square error in regression model as the evaluation indexes. The experimental results showed that the three indexes of the e-nose signal discrimination model established by the neural network algorithm were 100% in training set, correction set and prediction set, which were obviously better than the traditional decision tree, naive bayes, support vector machine, K nearest neighbor and boost classification, and could accurately differentiate the raw and vinegar products. Correlation coefficient and mean square error of the regression model in prediction set were 0.974 8 and 0.117 5 respectively, and could well predict curcumin compounds content in Curcuma kwangsiensis, and demonstrate the superiority of the simulation biometrics model in the analysis of traditional Chinese medicine. By BP neural network algorithm, e-nose odor fingerprint could quickly, conveniently and accurately realize the discrimination and regression, which suggested that more bionics information acquisition and identification patterns could be combined in the field of traditional Chinese medicine, so as to provide ideas and methods for the rapid evaluation and stan-dardization of the quality of traditional Chinese medicine.

Identification of curcumin content in raw and vinegar-processed rhizomes of Curcuma kwangsiensis based on electronic nose combined with back propagation neural network / 中国中药杂志

This study aimed to establish a rapid and accurate method for identification of raw and vinegar-processed rhizomes of Curcuma kwangsiensis, in order to predict the content of curcumin compounds for scientific evaluation. A complete set of bionics recognition mode was adopted. The digital odor signal of raw and vinegar-processed rhizomes of Curcuma kwangsiensis were obtained by e-nose, and analyzed by back propagation(BP) neural network algorithm, with the accuracy, the sensitivity and specificity in discriminant model, correlation coefficient as well as the mean square error in regression model as the evaluation indexes. The experimental results showed that the three indexes of the e-nose signal discrimination model established by the neural network algorithm were 100% in training set, correction set and prediction set, which were obviously better than the traditional decision tree, naive bayes, support vector machine, K nearest neighbor and boost classification, and could accurately differentiate the raw and vinegar products. Correlation coefficient and mean square error of the regression model in prediction set were 0.974 8 and 0.117 5 respectively, and could well predict curcumin compounds content in Curcuma kwangsiensis, and demonstrate the superiority of the simulation biometrics model in the analysis of traditional Chinese medicine. By BP neural network algorithm, e-nose odor fingerprint could quickly, conveniently and accurately realize the discrimination and regression, which suggested that more bionics information acquisition and identification patterns could be combined in the field of traditional Chinese medicine, so as to provide ideas and methods for the rapid evaluation and stan-dardization of the quality of traditional Chinese medicine.

Quantitative study on medicinal properties of traditional Chinese medicine based on BP neural network / 中草药

Objective: It is difficult to accurately grasp the essential characteristics of medicinal properties of traditional Chinese medicine due to the abstraction and vagueness. This paper proposes a Quantitative Model of Traditional Chinese Medicine's Properties based on BP Neural Network (QM-BP Model) to train and realize quantitative representations of Chinese herbal medicine (CHM). Methods: Data for analysis were obtained and organized by conceptual analysis. Sample pairs of the associations were obtained based on the relationships of CHM and their efficacy. Then a QM-BP model with three-tier structure in form of CHM-drug vector-efficacy was constructed, initialized and trained according to prior organized CHM data. Finally, rules of correlation of CHM and their efficacy was obtained by training dataset with drug vectors representing quantitative attributes of CHM. Results: Based on the training of QM-BP model, 474 TCM and 528 effects included in the textbook of TCM were trained and combined based on the training of QM-BP model. It was found that the BP drug vectors representing drug properties after training reflected the attribute characteristics of CHM better than the initial quantitative values. In addition, as BP drug vector and word vector have similar properties, the BP drug vectors for CHM with similar efficacy was relatively close in Euclidean distance while the CHM with different efficacies were relatively far in Euclidean distance. Conclusion: In this paper, a BP neural network was adopted to construct a medicine vector training model. Based on the correlation between the medicinal properties and efficacy of TCM, the quantified values of the medicinal properties were modified to represent medicinal properties more accurately. In future work, the QM-BP model can be applied to the analysis of herb pairs and prescriptions to analyze the rules of combination related to medicinal properties and the compatibility within prescriptions.

Study the therapeutic mechanism of Amomum compactum in gentamicin-induced acute kidney injury rat based on a back propagation neural network algorithm.

Acute kidney injury (AKI) is a major global public health problems, as it causes high morbidity and serious injury to renal function. However, the etiology for AKI is not very clear. In this study, a serum metabolite profile analysis was performed to identify potential biomarkers for gentamicin-induced AKI and to investigate the mechanism of action of Amomum compactum (AC) used for treatment. A metabonomics approach by ultra-performance liquid chromatography together with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was applied to perform the analysis. Back propagation (BP) neural network models were established for classifying data from the control, model, and AC-treated groups. Accuracy rate for classification was 91.7% in positive ion mode and 87.5% in negative ion mode. By orthogonal partial least squares discriminant analysis (OPLS-DA), 29 metabolites were identified as potential biomarkers of gentamicin-induced AKI. Most of them are related to phospholipid metabolism. After treatment with AC, the levels of sphingomyelin, sphingosine, phytosphingosine, and arachidonic acid were restored to normal. The results indicate that AC plays a protective role in rats with gentamicin-induced AKI via regulation of the phospholipid metabolic pathway. In this work, early biomarkers of AKI has been identified and underlying therapeutic mechanism of AC has been understood, therefore, AC can be further investigated and tested for clinical application.

Research on early-warning and evaluation of traditional Chinese medicine industry security based on BP neural network / 中草药

Through literature research, the safety evaluation index of traditional Chinese medicine (TCM) industry is identified, missing index value is calculated using the grey system theory, and the index weight is determined by the entropy weight method. The TCM industry safety from 2002 to 2014 was evaluated by grey relational analysis, and the TCM industry security evaluation index data from 2015 to 2020 are predicted using grey prediction and linear regression model method, combining the predicted value with historical data, TCM industry security BP neural network prediction model is established, and TCM industry security from 2015 to 2020 will be early warning. The results show that the next six years, TCM industry in China is safe, only mild prevention.

Design Optimization and Fabrication of High-Sensitivity SOI Pressure Sensors with High Signal-to-Noise Ratios Based on Silicon Nanowire Piezoresistors.

In order to meet the requirement of high sensitivity and signal-to-noise ratios (SNR), this study develops and optimizes a piezoresistive pressure sensor by using double silicon nanowire (SiNW) as the piezoresistive sensing element. First of all, ANSYS finite element method and voltage noise models are adopted to optimize the sensor size and the sensor output (such as sensitivity, voltage noise and SNR). As a result, the sensor of the released double SiNW has 1.2 times more sensitivity than that of single SiNW sensor, which is consistent with the experimental result. Our result also displays that both the sensitivity and SNR are closely related to the geometry parameters of SiNW and its doping concentration. To achieve high performance, a p-type implantation of 5 × 10¹8 cm-3 and geometry of 10 µm long SiNW piezoresistor of 1400 nm × 100 nm cross area and 6 µm thick diaphragm of 200 µm × 200 µm are required. Then, the proposed SiNW pressure sensor is fabricated by using the standard complementary metal-oxide-semiconductor (CMOS) lithography process as well as wet-etch release process. This SiNW pressure sensor produces a change in the voltage output when the external pressure is applied. The involved experimental results show that the pressure sensor has a high sensitivity of 495 mV/V·MPa in the range of 0⁻100 kPa. Nevertheless, the performance of the pressure sensor is influenced by the temperature drift. Finally, for the sake of obtaining accurate and complete information over wide temperature and pressure ranges, the data fusion technique is proposed based on the back-propagation (BP) neural network, which is improved by the particle swarm optimization (PSO) algorithm. The particle swarm optimization⁻back-propagation (PSO⁻BP) model is implemented in hardware using a 32-bit STMicroelectronics (STM32) microcontroller. The results of calibration and test experiments clearly prove that the PSO⁻BP neural network can be effectively applied to minimize sensor errors derived from temperature drift.

Research on Identification Model of Chinese Herbal Medicine by Texture Feature Parameter of Transverse Section Image / 世界科学技术-中医药现代化

This study was aimed to establish the classification method of Chinese herbal medicine based on feature parameters extracted from images of herbal transverse section, in order to explore the feasibility of automatic identi-fication method of herbal medicine. The extracted 26 parameters of 18 herbal medicine images by gray-level co-oc-currence matrix and grayscale gradient matrix were used as the basic data set. And the minimum covariance determi-nant (MCD) was used to delete the outliers. A total of 18 identification models were established using the native Bayes method and BP neural network methods. The results showed that the average correct rates of models were 90%. It was concluded the feasibility of using these models in the establishment of the automatic identification method of herbal medicines. It provided new technologies for the quantitative, scientific and objective identification of Chinese herbal medicine.