Amino acids are an essential group of compounds involved in protein synthesis and various metabolic and immune reactions in the human body. Chinese jujubes (Ziziphus jujuba Mill.) are an important fruit and medicinal plant which are native to China and have been introduced into around 50 countries. However, systematic research on the composition and content diversity of amino acids in the jujube is still lacking. In this experiment, the amino acid composition and the contents of the dominant amino acids in the fruit of 161 cultivars of jujube were determined by HPLC. Of the twenty-one kinds of amino acids detected, a total of fourteen kinds of amino acids were detected, of which eight kinds of amino acids were relatively high, including five essential amino acids (threonine, valine, isoleucine, leucine, and phenylalanine) and three nonessential amino acids (glycine, alanine, and proline). However, the contents of the remaining six amino acids were relatively low (aspartic acid, glutamic acid, histidine, serine, arginine, and tryptophan). Therefore, the eight primary amino acids were used as the index to evaluate the amino acids of 161 jujube varieties. Proline accounts for 56.8% of the total amino acid content among the eight amino acids. The total content of the eight primary amino acids in most jujube varieties was 1–1.5 g/100 g, and the highest content of ‘Zaoqiangmalianzao’ was 2.356 g/100 g. The average content of proline was 6.01–14.84 times that of the other seven amino acids. According to the WHO/FAO revised model spectrum of ideal essential amino acids for humans, 19 cultivars met the E/T (essential amino acids/total amino acids) standard, and their values ranged from 35% to 45%; 12 cultivars meet E/NE (non-essential amino acids) ≥ 60%. All cultivars reached the requirement of BC (branched–chain amino acids)/E ≥ 40% with 15 cultivars over 68%. One hundred and fifty-seven cultivars reach the standard of BC/A (aromatic amino acids) ≈ 3.0~3.5. The amino acid ratio coefficient analysis showed that phenylalanine was the first limiting amino acid of all the jujube cultivars. The SRC (the score of amino acid ratio coefficient) values of 134 cultivars were between 50% and 70%, with 12 cultivars over 70%, indicating that jujube fruits are of high nutritional value in terms of amino acids. Based on the principal component analysis and comprehensive ranking of amino acid nutritional value, the top five cultivars were screened from the 161 ones tested, i.e., ‘Tengzhouchanghongzao’, ‘Xinzhengxiaoyuanzao’, ‘Hanguowudeng’, ‘Xuputiansuanzao’, and ‘Lichengxiaozao’. This study established, firstly, a complete basic data analysis of amino acid content in jujube fruit which could be used to select germplasm resources suitable for developing functional amino acid food, and provide theoretical support for the high value utilization of amino acids in jujubes.

Background Plant shape and structure are important factors in peanut breeding research. Constructing a three-dimension (3D) model can provide an effective digital tool for comprehensive and quantitative analysis of peanut plant structure. Fast and accurate are always the goals of the plant 3D model reconstruction research. Results We proposed a 3D reconstruction method based on dual RGB-D cameras for the peanut plant 3D model quickly and accurately. The two Kinect v2 were mirror symmetry placed on both sides of the peanut plant, and the point cloud data obtained were filtered twice to remove noise interference. After rotation and translation based on the corresponding geometric relationship, the point cloud acquired by the two Kinect v2 was converted to the same coordinate system and spliced into the 3D structure of the peanut plant. The experiment was conducted at various growth stages based on twenty potted peanuts. The plant traits’ height, width, length, and volume were calculated through the reconstructed 3D models, and manual measurement was also carried out during the experiment processing. The accuracy of the 3D model was evaluated through a synthetic coefficient, which was generated by calculating the average accuracy of the four traits. The test result showed that the average accuracy of the reconstructed peanut plant 3D model by this method is 93.42%. A comparative experiment with the iterative closest point (ICP) algorithm, a widely used 3D modeling algorithm, was additionally implemented to test the rapidity of this method. The test result shows that the proposed method is 2.54 times faster with approximated accuracy compared to the ICP method. Conclusions The reconstruction method for the 3D model of the peanut plant described in this paper is capable of rapidly and accurately establishing a 3D model of the peanut plant while also meeting the modeling requirements for other species' breeding processes. This study offers a potential tool to further explore the 3D model for improving traits and agronomic qualities of plants.

Plant shape and structure are important factors in peanut breeding research. Constructing a three-dimension (3D) model can provide an effective digital tool for comprehensive and quantitative analysis of peanut plant structure. A 3D reconstruction method based on dual RGB-D cameras was proposed for the peanut plant 3D model quickly and accurately. The two Kinect v2 were mirror symmetry placed on both sides of the peanut plant, and the point cloud data obtained were filtered twice to remove noise interference. After rotation and translation based on the corresponding geometric relationship, the point cloud acquired by the two Kinect v2 was converted to the same coordinate system and spliced into the 3D structure of the peanut plant. The experiment was conducted at various growth stages based on twenty potted peanuts. The plant traits’ height, width, length, and volume were calculated through the reconstructed 3D models, and manual measurement was carried out at the same time. The accuracy of the 3D model was evaluated through a synthetic coefficient, which was generated by calculating the average accuracy of the four traits. The test result shows that the synthetic accuracy of the reconstructed peanut plant 3D model by this method is 93.42%. A comparative experiment with the iterative closest point (ICP) algorithm, a widely used 3D modeling algorithm, was additionally implemented to test the rapidity of this method. The test result shows that the proposed method is 2.54 times faster with approximated accuracy compared to the ICP method. This approach should be useful for 3D modeling and phenotyping peanut breeding.

Background: Chronic fatigue syndrome (CFS) is characterized by significant and persistent fatigue. Ginseng is a traditional anti-fatigue Chinese medicine with a long history in Asia, as demonstrated by clinical and experimental studies. Ginsenoside Rg1 is mainly derived from ginseng, and its anti-fatigue metabolic mechanism has not been thoroughly explored.Methods: We performed non-targeted metabolomics of rat serum using LC-MS and multivariate data analysis to identify potential biomarkers and metabolic pathways. In addition, we implemented network pharmacological analysis to reveal the potential target of ginsenoside Rg1 in CFS rats. The expression levels of target proteins were measured by PCR and Western blotting.Results: Metabolomics analysis confirmed metabolic disorders in the serum of CFS rats. Ginsenoside Rg1 can regulate metabolic pathways to reverse metabolic biases in CFS rats. We found a total of 34 biomarkers, including key markers Taurine and Mannose 6-phosphate. AKT1, VEGFA and EGFR were identified as anti-fatigue targets of ginsenoside Rg1 using network pharmacological analysis. Finally, biological analysis showed that ginsenoside Rg1 was able to down-regulate the expression of EGFR.Conclusion: Our results suggest ginsenoside Rg1 has an anti-fatigue effect, impacting the metabolism of Taurine and Mannose 6-phosphate through EGFR regulation. This demonstrates ginsenoside Rg1 is a promising alternative treatment for patients presenting with chronic fatigue syndrome.