ABSTRACT
Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021-2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R2) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.
ABSTRACT
The survival rate of non-Hodgkin lymphoma (NHL) has steadily improved. However, osteoporosis introduced by treatment is prevalent and associated with increased mortality and disability for patients with NHL. We aimed to investigate factors impacting bone mineral density (BMD) reduction and osteoporosis, and the trend of BMD after chemotherapy. Overall, 97 newly diagnosed patients with follicular lymphoma (FL) were retrospectively enrolled. CT attenuation values were measured to assess BMD levels. Although 73.2% of patients received calcium and vitamin D supplements, 44.3% showed significant BMD reduction, and baseline BMD and hemoglobin levels were the risk factors. 26.6% of patients newly developed osteoporosis post-chemotherapy where age and cumulative dose of glucocorticoid were risk factors. The results of 20 patients with consecutive follow-up showed that BMD continued to decline for 6 months post-chemotherapy and did not return to baseline values. Therefore, BMD evaluation and more positive anti-resorption treatments should be administered for high-risk patients.
ABSTRACT
This study aimed to determine the role of bacterial lipoprotein (BLP) in autophagy and apoptosis. Western blot was used to examine autophagy biomarkers in mouse bone marrow-derived macrophages (BMDMs) after infection with Salmonella typhimurium (S. typhimurium) and Staphylococcus aureus (S. aureus) and BLP stimulation. In BMDMs, enhanced protein expression of LC3-II was observed after S. typhimurium or S. aureus infection (P < 0.05) and BLP stimulation (P < 0.05). Autophagy inhibition by chloroquine resulted in increased levels of LC3-â ¡ and p62 protein (P < 0.05). Persistently upregulated expressions of Atg3 and Atg7 were observed following BLP stimulation (P < 0.05), and knockdown of Atg3 or Atg7 significantly attenuated BLP-enhanced protein expression of LC3-â ¡ in BMDMs. Furthermore, we found that the autophagy inhibitor 3-methyladenine prevented BLP- and infection-induced macrophage apoptosis. BLP is not only required for autophagy and apoptosis activation in macrophages but also for regulating the balance between autophagy and apoptosis.
ABSTRACT
Background: This study aimed to explore the association between serum amino acids (AAs) levels and bone mineral density (BMD). Methods: We performed a two-sample Mendelian randomization (MR) analysis to analyze the associations between the levels of eight AAs and BMD values by using summary-level genome-wide association study (GWAS) data. We applied the MR Steiger filtering method and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) global test to check for and remove single nucleotide polymorphisms (SNPs) that were horizontally pleiotropic. The associations were estimated with the inverse variance weighted (IVW), MR-Egger, weighted median and MR Robust Adjusted Profile Score (MR.RAPS) methods. Results: Our study found that genetically increased isoleucine (Ile) [IVW: effect = 0.1601, 95% confidence interval (CI) = 0.0604 ~ 0.2597, p = 0.0016] and valine (Val) levels (IVW: effect = 0.0953, 95% CI = 0.0251 ~ 0.1655, p = 0.0078) were positively associated with total body BMD (TB-BMD). The results also revealed that genetically increased tyrosine (Tyr) levels were negatively associated with TB-BMD (IVW: effect = -0.1091, 95% CI = -0.1863 ~ -0.0320, p = 0.0055). Conclusions: In this study, associations between serum AA levels and BMD were established. These findings underscore the important role that serum AAs play in the development of osteoporosis and provide evidence that osteoporosis can be prevented and treated by the intake of certain AAs.
