Relationship between lipids and sleep apnea: Mendelian randomization analysis.

BACKGROUND: Lipids increase the risk of sleep apnea; however, the causality between them is still inconclusive. AIM: To explore the causal relationship between serum lipids and sleep apnea using two-sample Mendelian randomization (MR) analysis. METHODS: Single nucleotide polymorphism (SNP) data related to serum lipids were obtained from the Global Lipids Genetics Consortium study, which included 188578 individuals of European ancestry. Additionally, sleep apnea-related SNP data were collected from the United Kingdom Biobank study, which comprised 463005 individuals of European ancestry. Two-sample MR analysis was performed to assess the causality between serum lipids and sleep apnea based on the above public data. RESULTS: Genetically predicted low-density lipoprotein (odds ratio [OR] = 0.99, 95% confidence interval [CI] = 0.99 to 1.00; P = 0.58), high-density lipoprotein (OR = 0.99, 95%CI = 0.99 to 1.00; P = 0.91), triglyceride (OR = 1.00, 95%CI = 0.99 to 1.00; P = 0.92), and total cholesterol (OR = 0.99, 95%CI = 0.99 to 1.00; P = 0.33) were causally unrelated to sleep apnea. CONCLUSION: Our MR analysis suggests that genetically predicted serum lipids are not risk factors of sleep apnea.

[Wheat leaf area index inversion using hyperspectral remote sensing technology].

The wheat leaf area index (LAI) was inverted using hyperspectral remote sensing technology in the present paper. Eighteen kinds of hyperspectral indices were comparatively analyzed, and the index OSAVI, which could reflect wheat LAI most sensitively, was screened out. The models for wheat LAI inversion were built using the field spectra as the training samples. The study showed that the calibration R-square and prediction R-square of the inversion model which were built by hyperspectral index OSAVI were 0.823 and 0.818, respectively, higher than that of other indices, indicating that the accuracy was highest. The inversion model was spatially quantitatively expressed in OMIS image, and then the inversion value and measured value was compared by the method of regression fitting. The R-square and RMSE of the fitting model were 0.756 and 0.500, respectively, indicating that the similarity between the inversion value and measured value was high. The result showed that it was feasible to invert the wheat LAI by hyperspectral indices, and OSVAI was an optimal one.