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Background: Sedentary behavior (SB) may contribute to obesity and lower extremity fluid retention, which may favor the development of obstructive sleep apnea (OSA). However, linking sedentary behavior to OSA is unclear. The purpose of this study was to determine if there is an association between SB and OSA. Methods: Three typical questions in the NHANES questionnaire(①The frequency of feeling excessively sleepy per month. ②The frequency of gasping, snorting or stopping breathing per week. ③The frequency of snoring per week.) have been used for the assessment of OSA. A physical activity questionnaire(On a typical day, the amount of time you spend sitting or reclining.) was used to assess SB. This secondary analysis included National Health and Nutrition Examination Survey (NHANES) participants (unweighted = 20,115). Weighted sample and multiple logistic regression complex sample analysis techniques were used in this study. Results: After adjustment for confounders, participants with SB(> 8 h/d) had a higher risk of OSA compared to SB(< 4 h/d). Stratified analysis by gender showed that there was no significant association of SB and OSA in men. However, in women, with SB(< 4 h/d) as the reference, participants with(≥ 4 h/d) had an increased risk of OSA. By age-stratified analysis, the association of SB with OSA was stronger among older participants. Conclusion: Analysis in this study showed a positive association between SB and OSA, more pronounced in women and participants older than 60 years old. [ABSTRACT FROM AUTHOR]

In this study, the feasibility of current retrieval from Sentinel-1 (S-1) synthetic aperture radar (SAR) in the radar look/range direction is investigated. S-1 Ocean (OCN) products acquired in interferometric wide (IW) mode in the regions with the western boundary current, i.e., the western Pacific and western Atlantic, are collected for the period from 2020 to 2022, which are collocated with the current field from HYbrid Coordinate Ocean Model (HYCOM). The OCN wind, HYCOM current, and Stokes drift estimated from the OCN wave parameters are geometric projected to be the range direction. In addition, the Doppler centroid anomaly (DCA) is estimated using the difference between the radar return Doppler frequency and the predicted Doppler shift, which are derived from the OCN products. The dependences of the upper ocean dynamics in the range direction on the DCA are studied, and it is found that the range Stokes drift, wind speed, and current speed are linearly related to the DCA. Based on deep learning, denoted as multi-layer perceptron, a range current retrieval algorithm from the OCN product is developed using two-thirds of the collocated dataset, and the root mean square error (RMSE) of the range current speed converges to 0.15 ms−1. In particular, the wave-induced surface Stokes drift is considered in the process. Comparison of one-third of the dataset yields an RMSE of 0.14 ms−1 for the range current speed, a correlation coefficient (r) of 0.85, and a bias of −0.001 ms−1. Validation against several moored buoys shows an RMSE of 0.12 ms−1 with a r of 0.74 and a bias of −0.01 ms−1. Under this circumstance, it is believed that the algorithm used in this study is applicable for range current retrieval from the S-1 OCN product. [ABSTRACT FROM AUTHOR]

The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research. [ABSTRACT FROM AUTHOR]