Revealing quality chemicals of Tetrastigma hemsleyanum roots in different geographical origins using untargeted metabolomics and random-forest based spectrum-effect analysis.

Tetrastigma hemsleyanum root is a popular functional food in China, and the price varies based on the origin of the product. The link between the origin, metabolic profile, and bioactivity of T. hemsleyanum must be investigated. This study compares the metabolic profiles of 254 samples collected from eight different areas with 49 potential key chemical markers using plant metabolomics. The metabolic pathways of the five critical flavonoid metabolites were annotated and enriched using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Moreover, a random forest model aiding the spectrum-effect relationship analysis was developed for the first time indicating catechin and darendoside B as potential quality markers of antioxidant activity. The findings of this study provide a comprehensive understanding of the chemical composition and bioactive compounds of T. hemsleyanum as well as valuable information on the evaluation of the quality of various samples and products in the market.

A ROI Extraction Method for Wrist Imaging Applied in Smart Bone-Age Assessment System.

Bone Age (BA) is reckoned to be closely associated with the growth and development of teenagers, whose assessment highly depends on the accurate extraction of the reference bone from the carpal bone. Being uncertain in its proportion and irregular in its shape, wrong judgment and poor average extraction accuracy of the reference bone will no doubt lower the accuracy of Bone Age Assessment (BAA). In recent years, machine learning and data mining are widely embraced in smart healthcare systems. Using these two instruments, this paper aims to tackle the aforementioned problems by proposing a Region of Interest (ROI) extraction method for wrist X-ray images based on optimized YOLO model. The method combines Deformable convolution-focus (Dc-focus), Coordinate attention (Ca) module, Feature level expansion, and Efficient Intersection over Union (EIoU) loss all together as YOLO-DCFE. With the improvement, the model can better extract the features of irregular reference bone and reduce the potential misdiscrimination between the reference bone and other similarly shaped reference bones, improving the detection accuracy. We select 10041 images taken by professional medical cameras as the dataset to test the performance of YOLO-DCFE. Statistics show the advantages of YOLO-DCFE in detection speed and high accuracy. The detection accuracy of all ROIs is 99.8 %, which is higher than other models. Meanwhile, YOLO-DCFE is the fastest of all comparison models, with the Frames Per Second (FPS) reaching 16.

A comprehensive strategy for prediction and quality evaluation of standardized planting herbs based on plant metabolomics coupled with extreme learning machine: Astragali Radix as an example.

INTRODUCTION: Standardizing the planting process is an effective way to control the quality stability of herbal resources, which are susceptible to external environmental factors (e.g., moisture, soil, etc.). However, how to scientifically and comprehensively assess the effects of standardized planting on plant quality and quickly test unknown samples has not been addressed. OBJECTIVE: The aim of this study was to determine and compare the metabolite levels of herbs before and after standardized planting, to quickly distinguish their sources, and to evaluate their quality, using the typical herb Astragali Radix (AR) as an example. METHODS: In this study, an efficient strategy using liquid chromatography-mass spectrometry (LC-MS) based on plant metabolomics combined with extreme learning machine (ELM) has been developed to efficiently distinguish and predict AR after standardized planting. Moreover, a comprehensive multi-index scoring method has been developed for the comprehensive evaluation of the quality of AR. RESULTS: The results confirmed that AR after standardized planting was significantly differentiated, with a relatively stable content of 43 differential metabolites, mainly including flavonoids. An ELM model was established based on LC-MS data, and the accuracy in predicting unknown samples could reach more than 90%. As expected, higher total scores were obtained for AR after standardized planting, indicating much better quality. CONCLUSION: A dual system for evaluating the impact of standardized planting on the quality of plant resources has been established, which will significantly contribute to innovation in the quality evaluation of medicinal herbs and support the selection of optimal planting conditions.

Study of Multidimensional and High-Precision Height Model of Youth Based on Multilayer Perceptron.

Predicting the adult height of children accurately has great social value for the selection of outstanding athlete as well as early detection of children's growth disorders. Currently, the mainstream method used to predict adult height in China has three problems: its standards are not uniform; it is stale for current Chinese children; its accuracy is not satisfactory. This article uses the data collected by the Chinese Children and Adolescents' Physical Fitness and Growth Health Project in Zhejiang primary and secondary schools. We put forward a new multidimensional and high-precision youth growth curve prediction model, which is based on multilayer perceptron. First, this model uses multidimensional growth data of children as predictors and then utilizes multilayer perceptron to predict the children's adult height. Second, we find the Table of Height Standard Deviation of Chinese Children and fit the data of zero standard deviation to obtain the curve. This curve is regarded as Chinese children's mean growth curve. Third, we use the least-squares method and the mean curve to calculate the individual growth curve. Finally, the individual curve can be used to predict children's state height. Experimental results show that this adult height prediction model's accuracy (between 2 cm) of boys and girls reached 90.20% and 88.89% and the state height prediction accuracy reached 77.46% and 74.93%. Compared with Bayley-Pinneau, the adult height prediction is improved 19.61% for boys and 13.33% for girls. Compared with BoneXpert, the adult height prediction is improved 25.49% for boys and 6.67% for girls. Compared with the method based on the bone age growth map, the adult height prediction is improved 15.69% for boys and 24.45% for girls.

Design of Growth Trend Map of Children and Adolescents Based on Bone Age.

Accurate height prediction has important reference significance for the development of children and adolescents and the selection of athletes. The current mainstream height prediction methods include the B-P (Bayley-Pinneau) method and the TW2 (Tanner-Whitehouse) method. A large number of documents show that the B-P method and the TW2 method have relatively large deviations in the lifelong height prediction results of Chinese children and adolescents. Based on the data collected by the Chinese Adolescent Students' Physical Fitness and Growth and Development Health Project in Zhejiang's primary and secondary schools, this paper proposes a graph of height growth trends based on bone age. The height map of age has more reference value. Aiming at the feasibility of the height data in the statistical results, the interpolation prediction method is used to verify the data, and the height growth trend graph is drawn through the method of fitting. Validation results with actual data show that the average error of the lifetime height prediction of the height growth trend map proposed in this paper is 2.1 cm, which is 1.4 cm lower than the 3.5 cm error predicted by the B-P method and 0.4 cm lower than the 2.5 cm error predicted by the TW2 method.

Human action recognition based on HOIRM feature fusion and AP clustering BOW.

In this paper, we propose a human action recognition method using HOIRM (histogram of oriented interest region motion) feature fusion and a BOW (bag of words) model based on AP (affinity propagation) clustering. First, a HOIRM feature extraction method based on spatiotemporal interest points ROI is proposed. HOIRM can be regarded as a middle-level feature between local and global features. Then, HOIRM is fused with 3D HOG and 3D HOF local features using a cumulative histogram. The method further improves the robustness of local features to camera view angle and distance variations in complex scenes, which in turn improves the correct rate of action recognition. Finally, a BOW model based on AP clustering is proposed and applied to action classification. It obtains the appropriate visual dictionary capacity and achieves better clustering effect for the joint description of a variety of features. The experimental results demonstrate that by using the fused features with the proposed BOW model, the average recognition rate is 95.75% in the KTH database, and 88.25% in the UCF database, which are both higher than those by using only 3D HOG+3D HOF or HOIRM features. Moreover, the average recognition rate achieved by the proposed method in the two databases is higher than that obtained by other methods.