Appearance, components, pasting, and thermal characteristics of chalky grains of rice varieties with varying protein content.

This study investigated two rice varieties, GuichaoII and Jiazao311, with distinct protein content to determine the variation in appearance, components, pasting, and thermal properties of rice with different chalkiness degrees. Grain length, width, head rice weight, and whiteness of both varieties markedly increased as chalkiness increased from 0% to 50%. However, the variation in components, pasting, and thermal characteristics of chalky grain substantially differed between the rice varieties. The protein content of GuichaoII (low protein content) significantly increased with the chalkiness degree, along with a significant increase in onset, peak, and conclusion temperatures and gelatinization enthalpy. In Jiazao311 (high protein content), the chalkiness degree increased with the protein content but decreased with the starch content, along with increased trough, final, setback, and consistency viscosities. Compared to amylose content, protein content had a greater influence on the thermal properties and pasting characteristics of chalky grains of GuichaoII and Jiazao311, respectively.

Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice.

Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R2) of 0.156-0.452, 0.357, 0.160-0.460, 0.192-0.746, 0.453-0.708, and 0.469-0.880, respectively, which were improved to 0.675-0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574-1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice.

Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model.

Accurate and rapid identification of the effective number of panicles per unit area is crucial for the assessment of rice yield. As part of agricultural development, manual observation of effective panicles in the paddy field is being replaced by unmanned aerial vehicle (UAV) imaging combined with target detection modeling. However, UAV images of panicles of curved hybrid Indica rice in complex field environments are characterized by overlapping, blocking, and dense distribution, imposing challenges on rice panicle detection models. This paper proposes a universal curved panicle detection method by combining UAV images of different types of hybrid Indica rice panicles (leaf-above-spike, spike-above-leaf, and middle type) from four ecological sites using an improved You Only Look Once version 4 (YOLOv4) model. MobileNetv2 is used as the backbone feature extraction network based on a lightweight model in addition to a focal loss and convolutional block attention module for improved detection of curved rice panicles of different varieties. Moreover, soft non-maximum suppression is used to address rice panicle occlusion in the dataset. This model yields a single image detection rate of 44.46 FPS, and mean average precision, recall, and F1 values of 90.32%, 82.36%, and 0.89%, respectively. This represents an increase of 6.2%, 0.12%, and 16.24% from those of the original YOLOv4 model, respectively. The model exhibits superior performance in identifying different strain types in mixed and independent datasets, indicating its feasibility as a general model for detection of different types of rice panicles in the heading stage.

Improving obesity and lipid metabolism using conjugated linoleic acid.

BACKGROUND: Conjugated linoleic acid (CLA) can prevent fatty acid accumulation induced by a high-fructose diet and improve lipid metabolism disorders in patients. OBJECTIVES: We aimed to investigate the effect of CLA on obesity and lipid metabolism and its possible mechanism. METHODS: Eight-month-old male BKS.Cg-Dock7m +/+ Leprdb /JNju (db/db) mice (n = 12) were fed a CLA mix composed of equivalent c9, t11-CLA and t10, c12-CLA for 1 month. The effect of CLA on body weight, water and food intake, and triglyceride (TG) and total cholesterol (TC) levels was investigated. PPARα, PPARγ and CD36 expression was determined by quantitative PCR and western blotting. Additionally, the expression of these three genes was studied in HepG2 cells treated with CLA and linoleic acid. RESULTS: CLA treatment notably reduced the dietary and water intake of db/db mice, effectively reduced body weight, and decreased serum TG and TC levels (p < 0.05). Increased expression of PPARα (p < 0.05) and decreased expression of CD36 (p < 0.001) were observed in the liver of mice that were fed CLA. CLA increased PPARα expression (p < 0.001) and decreased PPARγ (p < 0.001) and CD36 expression (p < 0.01) in HepG2 cells. CONCLUSIONS: Our results showed that CLA can improve lipid metabolism in obese mice through upregulation of PPARα expression and downregulation of CD36 expression.

Low Light Stress Increases Chalkiness by Disturbing Starch Synthesis and Grain Filling of Rice.

Low light stress increases the chalkiness of rice; however, this effect has not been fully characterized. In this study, we demonstrated that low light resulted in markedly decreased activity of ADP-glucose pyrophosphorylase in the grains and those of sucrose synthase and soluble starch synthase in the early period of grain filling. Furthermore, low light also resulted in decreased activities of granule-bound starch synthase and starch branching enzyme in the late period of grain filling. Therefore, the maximum and mean grain filling rates were reduced but the time to reach the maximum grain filling rates and effective grain filling period were increased by low light. Thus, it significantly decreased the grain weight at the maximum grain filling rate and grain weight and retarded the endosperm growth and development, leading to a loose arrangement of the amyloplasts and an increase in the chalkiness of the rice grains. Compared to the grains at the top panicle part, low light led to a greater decrease in the grain weight at the maximum grain filling rate and time to reach the grain weight at the maximum grain filling rate at the bottom panicle part, which contributed to an increase in chalkiness by increasing the rates of different chalky types at the bottom panicle part. In conclusion, low light disturbed starch synthesis in grains, thereby impeding the grain filling progress and increasing chalkiness, particularly for grains at the bottom panicle part.