Artificial irrigation is critical for improving soil moisture conditions and ensuring crop growth. Its irrational deployment can lead to ecological and environmental issues. Mapping and understanding the changes in irrigated areas are vital to effectively managing limited water. However, most researchers map irrigated areas with a single data resource, which makes it hard to detect irrigated signals in complex situations. The case study area for this paper was China’s winter wheat region, and an irrigated area map was generated by analyzing the effects of artificial irrigation on crop phenological characteristics and soil moisture time series. The mapping process involved three steps: (1) generating a basic irrigated map by employing the ISODATA classification method on the Kolmogorov–Smirnov test irrigation signals from the microwave remote sensing data and reanalysis data; (2) creating the other map with the maximum likelihood ratio classification and zoning scheme on the phenological parameters extracted from the NDVI time series; and (3) fusing these two maps at the decision level to obtain the final map with a higher spatial resolution of 1 km. The map was evaluated against existing irrigated area data and was highly compatible with GMIA 5.0. The overall accuracy (OA) was 73.49%.
Objective To explore the association between metabolic syndrome (MetS) and its component and thyroid volume in Chinese adolescents, and to compare the detection rate of MetS under the three different diagnostic criteria. Methods A total of 1097 school students (610 males and 487 females, ages 12–15 years) were enrolled. All the participants underwent physical examination, biochemical test, and thyroid gland ultrasonography. The thyroid volume of normal, overweight and obese group was compared. We also analyzed the association between the number of MetS components and thyroid volume. Linear and multiple linear regression were applied to explore the association between metabolic parameters and thyroid volume. Results The thyroid volume of the males in overweight (t = 3.784, P < 0.001) and obese group (t = 5.068, P < 0.001) was significantly larger than that in normal group; the thyroid volume of the females in overweight group (t = 4.627,P < 0.001) was significantly larger than that of normal group. As the number of MetS components increased, the thyroid volume also increased significantly (F = 10.64, P < 0.01). Height, weight, body mass index (BMI), waist circumference, hip circumference, systolic blood pressure, fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), uric acid and triglyceride were all positively associated with thyroid volume in the adolescents (P all < 0.001). Meanwhile, there was a negative association between high-density lipoprotein cholesterol (HDL-C) and thyroid volume (P < 0.001). According to multiple linear regression, waist circumference (β = 0.029, 95 %CI: 0.015 ~ 0.042; P < 0.01) and waist height ratio (β = 3.317, 95 %CI: 1.661 ~ 4.973; P < 0.01) were predict factors of thyroid volume. No statistical difference was found in the detection rates of metabolic syndrome under the three diagnostic criteria. Conclusions Overweight, obesity and metabolic syndrome was associated with adolescent thyroid volume. Central obesity may be an independent risk factor for thyroid enlargement in adolescents.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.