Understanding elevated CO2 concentrations in East Asia relative to the global mean during boreal spring on the slow and interannual timescales.

It is important to examine the physical processes that regulate current CO2 concentrations in East Asia to understand the global carbon cycle. To do this, we begin by defining the difference between East Asian and global CO2 concentrations (East Asian CO2 concentration minus global CO2 concentration), which is referred to as East Asian local CO2 concentration (i.e., EA_LCO2). Then, we examine the physical processes associated with the variability of EA_LCO2 during boreal spring (March-April-May) on the slow and interannual timescales. Our results indicate that there are two key factors leading to elevated CO2 concentrations in East Asia relative to the global mean during boreal spring; one is higher emissions in East Asia, which mostly explains the increasing in EA_LCO2 on the slow timescales. The other is a cool sea surface temperature (SST) in the eastern tropical Pacific (La-Nina-like SST), which is associated with an interannual higher CO2 concentration in East Asia than the global mean. Enhanced convective activity in the western tropical Pacific, which is associated with a La-Nina-like SST forcing, induces low-pressure circulation in the western North Pacific with northerly winds, leading to suppressed precipitation and cool surface temperature in East Asia. Subsequently, those suppress vegetation growth as well as gross primary product, resulting in relatively high CO2 concentrations in East Asia compared to the global mean.

Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample-A Study with 39,991,680 Cases.

Metabolic syndrome can cause complications, such as stroke and cardiovascular disease. We aimed to propose a nomogram that visualizes and predicts the probability of metabolic syndrome occurrence after identifying risk factors related to metabolic syndrome for prevention and recognition. We created a nomogram related to metabolic syndrome in this paper for the first time. We analyzed data from the Korea National Health and Nutrition Examination Survey VII. Total 17,584 participants were included in this study, and the weighted sample population was 39,991,680, which was 98.1% of the actual Korean population in 2018. We identified 14 risk factors affecting metabolic syndrome using the Rao-Scott chi-squared test. Next, logistic regression analysis was performed to build a model for metabolic syndrome and 11 risk factors were finally obtained, including BMI, marriage, employment, education, age, stroke, sex, income, smoking, family history and age* sex. A nomogram was constructed to predict the occurrence of metabolic syndrome using these risk factors. Finally, the nomogram was verified using a receiver operating characteristic curve (ROC) and a calibration plot.