Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Basic Disciplines of Agriculture submitted time 2023-08-14 Cooperative journals: 《智慧农业（中英文）》
Abstract： [Objective] Leaf chlorophyll content (LCC) of flue-cured Tobacco is an important indicator for characterizing the photosynthesis, nutritional status, and growth of the crop. Tobacco is an important economic crop with leaves as the main harvest object, it is crucial to monitor its LCC. Hyperspectral data can be used for the rapid estimation of LCC in flue-cured tobacco leaves, making it of great significance and application value. The purpose of this study was to efficiently and accurately estimate the LCC of flue-cured tobacco during different growth stages. [Methods] Zhongyan 100 was chose as the research object, five nitrogen fertilization levels were set. In each plot, three plants were randomly and destructively sampled, resulting in a total of 45 ground samples for each data collection. After transplanting, the reflectance data of the flue-cured tobacco canopy at six growth stages (32, 48, 61, 75, 89, and 109 d ) were collected using a UAV equipped with a Resonon Pika L hyperspectral. Spectral indices for the LCC estimation model of flue-cured tobacco were screened in two ways: (1) based on 18 published vegetation indices sensitive to LCC of crop leaves; (2) based on random combinations of any two bands in the wavelength range of 400‒1000 nm. The Difference Spectral Index (DSI), Ratio Spectral Index (RSI), and Normalized Spectral Index (NDSI) were calculated and plotted against LCC. The correlations between the three spectral indices and leaf LCC were calculated and plotted using contour maps. Five regression models, unary linear regression (ULR), multivariable linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR), were used to estimate the chlorophyll content. A regression estimate model of LCC based on various combinations of spectral indices was eventually constructed by comparing the prediction accuracies of single spectral index models multiple spectral index models at different growth stages. [Results and Discussions] The results showed that the LCC range for six growth stages was 0.52‒2.95 mg/g. The standard deviation and coefficient of variation values demonstrated a high degree of dispersion in LCC, indicating differences in fertility between different treatments at the test site and ensuring the applicability of the estimation model within a certain range. Except for 109 d after transplanting, most vegetation indices were significantly correlated with LCC (p<0.01). Compared with traditional vegetation indices, the newly combined spectral indices significantly improved the correlation with LCC. The sensitive bands at each growth stage were relatively concentrated, and the spectral index combinations got high correlation with LCC were mainly distributed between 780‒ 940 nm and 520‒710 nm. The sensitive bands for the whole growth stages were relatively dispersed, and there was little difference in the position of sensitive band between different spectral indices. For the univariate LCC estimation model, the highest modeling accuracy was achieved using the newly combined Normalized Spectral Index and Red Light Ratio Spectral Index at 75 d after transplanting. The coefficients of determination (R2) and root mean square errors (RMSE) for the modeling and validation sets were 0.822, 0.814, and 0.226, 0.230, respectively. The prediction results of the five resgression models showed that the RFR algorithm based on multivariate data performed best in LCC estimation. The R2 and RMSE of the modeling set using data at 75 d after transplanting were 0.891 and 0.205, while those of the validation set reached 0.919 and 0.146. In addition, the estimation performance of the univariate model based on the whole growth stages dataset was not ideal, with R2 of 0.636 and 0.686, and RMSE of 0.333 and 0.304 for the modeling and validation sets, respectively. However, the estimation accuracy of the model based on multiple spectral parameters was significantly improved in the whole growth stages dataset, with R2 of 0.854 and 0.802, and RMSE of 0.206 and 0.264 for the modeling and validation sets of the LCC-RFR model, respectively. In addition, in the whole growth stages dataset, the estimation accuracy of the LCC-RFR model was better than that of the LCC-MLR, LCC-PLSR, and LCC-SVR models. Compared with the modeling set, R2 increased by 19.06%, 18.62%, and 29.51%, while RMSE decreased by 31.93%, 29.51%, and 28.24%. Compared with the validation set, R2 increased by 8.21%, 12.62%, and 8.17%, while RMSE decreased by 3.76%, 9.33%, and 4.55%. [Conclusions] The sensitivity of vegetation indices (VIs) to LCC is closely connected to the tobacco growth stage, according to the results this study, which examined the reaction patterns of several spectral indices to LCC in flue-cured tobacco. The sensitivity of VIs to LCC at various growth stages is critical for crop parameter assessment using UAV hyperspectral photography. Five estimation models for LCC in flue-cured tobacco leaves were developed, with the LCC-RFR model demonstrating the greatest accuracy and stability. The RFR model is less prone to overfitting and can efficiently decrease outlier and noise interference. This work could provide theoretical and technological references for LCC estimate and flue-cured tobacco growth monitoring.
Subjects: Medicine, Pharmacy >> Preclinical Medicine submitted time 2017-12-07 Cooperative journals: 《南方医科大学学报》
Abstract： Objective To compare the biological behaviors of two drug-resistant testicular cancer cell lines established by different methods. Methods Drug-resistance was induced in testicular cancer cell lines exposure of the cells to increasing concentrations of or a high dose of cisplatin (I-10/DDPi and I-10/DDPh cell lines, respectively). The morphological characteristics of the two cell lines were observed microscopically. The resistance index of the cells was determined with MTT assay, and the cell growth curves were drawn. The cellular expression of resistance-associated proteins MDR1 and P-gp was detected by Western blotting. The cell invasion ability was assessed with Transwell assay. Results Normal testicular cancer cell line I-10 and the two resistant cell lines all showed an adherent growth pattern. Compared with I-10 cells, I-10/DDP cells exhibited slightly heterogenous cell sizes, irregular shapes, the presence of microvilli tentacles on the cell surface, and a scattered arrangement. The cisplatin resistance index of I-10/DDPi and I-10/DDPh cells were 3.924 and 3.099, respectively. Compared with I-10, the drug-resistant cell lines showed extended doubling time with increased expressions of MDR1 and P-gp and increased cell invasiveness, which was especially obvious in I-10/DDPi cells. Conclusion Both increasing dose exposure and high-dose exposure to cisplatin can induce cisplatin resistance in testicular cancer cells, and the resistant cells established by the latter method better mimics clinical drug-resistant tumor cells.
Subjects: Biology >> Zoology submitted time 2017-10-10 Cooperative journals: 《动物营养学报》
Abstract：本试验的目的是探讨饲粮中添加番茄渣对育肥猪生长性能、胴体性状、肉品质和抗氧化能力的影响。采用单因素试验设计，将80头平均体重为（95.20±3.95） kg的“杜×长×大”三元杂交育肥母猪随机分成4组，每组4个重复，每个重复5头。对照组饲喂基础饲粮，TOP100组、TOP200组、TOP300组分别饲喂基础饲粮+100 g/(头·d)番茄渣、基础饲粮+200 g/(头·d)番茄渣、基础饲粮+300 g/(头·d)番茄渣。预试期3 d，正试期37 d。结果显示：1）TOP100组平均增重和日增重极显著高于其他3组（P＜0.01）。2）与对照组相比，TOP200组的胴体重显著增加（P＜0.05）；TOP100组的胴体长显著降低（P＜0.05）；TOP100组、TOP200组、TOP300组的眼肌面积分别增加了49.11%、46.82%、71.93%（P＜0.05）。3）与对照组相比，TOP200组的肌肉红度值极显著升高(P＜0.01)，TOP100组的肌肉蒸煮损失减少了15.17%(P＜0.05)，TOP100组、TOP200组的肌肉离心失水率极显著降低(P＜0.01)。4）与对照组相比，TOP300组的肝脏总抗氧化能力显著升高(P＜0.05)；TOP200组的肝脏丙二醛含量显著降低(P＜0.05)；TOP300组的肌肉总超氧化物歧化酶活性显著降低(P＜0.05)；TOP100组、TOP200组、TOP300组的肌肉丙二醛含量显著降低(P＜0.05)。以上研究结果表明，饲粮中添加100 g/(头·d)番茄渣能显著提高育肥猪的平均日增重且降低料重比，改善胴体性状和肉品质，并提高肝脏和肌肉抗氧化能力；此外，番茄渣在育肥猪饲粮中的添加量每天每头不宜超过300 g。
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