Soil Moisture Retrieval Using BuFeng-1 A/B Based on Land Surface Clustering Algorithm

A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity and SM. The global comparison between the results and SM product from the Soil Moisture Active Passive mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm3·cm-3. The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm3·cm-3. This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 41971377). China Spacesat Company, Ltd. ESA-MOST China Dragon5 Programme (Grant Number: ID.58070) 10.13039/501100003392-Natural Science Foundation of Fujian Province (Grant Number: 2019J01853

Artificial Cold Air Increases the Cardiovascular Risks in Spontaneously Hypertensive Rats

The purpose was to investigate the effects of artificial cold air on cardiovascular risk in hypertensive subjects. An artificial cold air was simulated with hourly ambient temperature data of a real moderate cold air in China. Twenty-four male SHR rats were randomly divided into the minimum temperature (Tmin) group, the rewarming temperature (Tr) group and two concurrent control groups with six rats in each (Tmin and Tr represent two cold air time points, respectively). Tmin and Tr groups were exposed to the cold air that was stopped at Tmin and Tr, respectively. After cold air exposure, blood pressure, heart rate and body weight were monitored, blood was collected for the detection of some indexes like fibrinogen, total cholesterol and uric acid. Results demonstrated that blood pressure, whole blood viscosity, blood fibrinogen, total cholesterol and uric acid increased significantly both in the Tmin and Tr groups; low density lipoprotein/high density lipoprotein increased significantly only in Tr group; there was higher level of blood fibrinogen in the Tr group than the Tmin group; higher levels of creatine kinase-MB was found in both the Tmin and Tr groups. These results suggest that cold air may increase the cardiovascular risks in hypertensive subjects indirectly through its effects on the sympathetic nervous system and renin angiotensin system, blood pressure and atherosclerosis risk factors like blood viscosity and fibrinogen, lipids and uric acid in the blood

Effects of Cold Air on Cardiovascular Disease Risk Factors in Rat

The purpose of this study is to explore possible potential implications of cold air in cardiovascular disease (CVD) risk in rats. Healthy <em>Wistar</em> rats were exposed to artificial cold air under laboratory conditions, and their systolic blood pressure, heart rate, vasoconstriction, CVD risk factors, and myocardial damage indicators after cold air exposure were determined and evaluated. Systolic blood pressure, whole blood viscosity, and plasma level of norepinephrine, angiotensinⅡ, low density lipoprotein, total cholesterol, and fibrinogen in treatment groups increased significantly compared with control groups. No significant variations were found in plasma Mb and cTnT and myocardial tissue between the treatment and control groups. Results indicate that: (1) higher levels of SBP, WBV and LDL/HDL, total cholesterol (TC), and FG in blood may indicate higher CVD risks during cold air exposure; (2) cold air may exert continuous impacts on SBP and other CVD risk factors

Super Resolution Mapping of Scatterometer Ocean Surface Wind Speed Using Generative Adversarial Network: Experiments in the Southern China Sea

This paper designed a Generative Adversarial Network (GAN)-based super-resolution framework for scatterometer ocean surface wind speed (OSWS) mapping. An improved GAN, WSGAN, was well-trained to generate high-resolution OSWS (~1/64 km) from low-resolution OSWS (~12.5 km) retrieved from scatterometer observations. The generator of GAN incorporated Synthetic Aperture Radar (SAR) information in the training phase. Therefore, the pre-trained model could reconstruct high-resolution OSWS with historical local spatial and texture information. The training experiments were executed in the South China Sea using the OSWS generated from the Advanced SCATterometer (ASCAT) scatterometer and Sentinel-1 SAR OSWS set. Several GAN-based methods were compared, and WSGAN performed the best in most sea states, enabling more detail mining with fewer checkerboard artifacts at a scale factor of eight. The model reaches an overall root mean square error (RMSE) of 0.81 m/s and an overall mean absolute error (MAE) of 0.68 m/s in the collocation region of ASCAT and Sentinel-1. The model also exhibits excellent generalization capability in another scatterometer with an overall RMSE of 1.11 m/s. This study benefits high-resolution OSWS users when no SAR observation is available

Linking microbial community compositions to cotton nitrogen utilization along soil salinity gradients

Accumulating evidence has shown that soil salinization is a global threat to microbial functional diversity and crop growth. However, the effects of microorganisms on crop growth, yield components, and nitrogen (N) utilization under salt stress are poorly understood. In this research, 16S rRNA, quantitative polymerase chain reaction (qPCR) sequencing, and 15N isotopic tracer method were used to identify associations between microbial community composition and cotton N utilization across four salinity gradients (control, 0–2 dS m−1; low-salinity, 2–4 dS m−1; mid-salinity, 4–8 dS m−1; and high-salinity, 8–15 dS m−1). These findings indicated that salinity is the primary driving force behind environmental degradation. Furthermore, the results showed that the effect of microbial community diversity on cotton 15N utilization was regulated by soil electrical conductivity. The promotional effect of Proteobacteria on cotton 15N utilization was gradually enhanced with increasing soil salinity, while the relative abundance of Actinomycetes first showed an increasing trend after which it decreased. The present study confirms that Actinobacteria dwelling in arid areas are capable of growing under selective soil salinity gradients. This study highlights the importance of focusing on microbial community diversity for cotton growth and yield, and for the first time to clarifying the differences in cotton N utilization under different soil salinity gradients in arid areas. In addition, the association between edaphic properties and cotton parts (rhizospheres, stems, leaves, and fruits) was evaluated more in-depth using the mediating and moderating effects model in the present study. Further investigations are required to ascertain whether salt-tolerant bacterial communities in the rhizosphere can be isolated to promote N utilization by cotton

A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points

The present study developed a hybrid semi-automatic method to extract the liver from abdominal computerized tomography (CT) images. The proposed hybrid method consists of a customized fast-marching level-set method for detection of an optimal initial liver region from multiple seed points selected by the user and a threshold-based level-set method for extraction of the actual liver region based on the initial liver region. The performance of the hybrid method was compared with those of the 2D region growing method implemented in OsiriX using abdominal CT datasets of 15 patients. The hybrid method showed a significantly higher accuracy in liver extraction (similarity index, SI= 97.6 +/- 0.5%; false positive error, FPE= 2.2 +/- 0.7%; false negative error, FNE= 2.5 +/- 0.8%; average symmetric surface distance, ASD = 1.4 +/- 0.5 mm) than the 2D (SI = 94.0 +/- 1.9%; FPE= 5.3 +/- 1.1%; FNE= 6.5 +/- 3.7%; ASD =6.7 +/- 3.8 mm) region growing method. The total liver extraction time per CT dataset of the hybrid method (77 +/- 10 s) is significantly less than the 2D region growing method (575 +/- 136 s). The interaction time per CT dataset between the user and a computer of the hybrid method (28 +/- 4 s) is significantly shorter than the 2D region growing method (484 +/- 126 s). The proposed hybrid method was found preferred for liver segmentation in preoperative virtual liver surgery planning. (C) 2013 Elsevier Ireland Ltd. All rights reserved.X112326sciescopu