Application of China-Brazil Earth resources satellite in China

The launch and successful operation of Chinese Brazil Earth resources satellite (CBERS-1) in China has accelerated the application of space technology in China. These applications include agriculture, forestry, water conservation. land resources, city planning, environment protection and natural hazards monitoring and so oil. The result of these applications provides a scientific basis for government decision making and has created great economic and social benefits in Chinese national economy construction. In this paper we present examples and provide auxiliary documentation of additional applications of the data from Earth resource monitoring, (C) 2009 COSPAR. Published by Elsevier Ltd. All rights reserved

Large chemokine binding spectrum of human and mouse atypical chemokine receptor GPR182 (ACKR5)

Atypical chemokine receptors (ACKRs) play pivotal roles in immune regulation by binding chemokines and regulating their spatial distribution without inducing G-protein activation. Recently, GPR182, provisionally named ACKR5, was identified as a novel ACKR expressed in microvascular and lymphatic endothelial cells, with functions in hematopoietic stem cell homeostasis. Here, we comprehensively investigated the chemokine binding profile of human and mouse GPR182. Competitive binding assays using flow cytometry revealed that besides CXCL10, CXCL12 and CXCL13, also human and mouse CXCL11, CXCL14 and CCL25, as well as human CCL1, CCL11, CCL19, CCL26, XCL1 and mouse CCL22, CCL24, CCL27 and CCL28 bind with an affinity of less than 100 nM to GPR182. In line with the binding affinity observed in vitro, elevated serum levels of CCL22, CCL24, CCL25, and CCL27 were observed in GPR182-deficient mice, underscoring the role of GPR182 in chemokine scavenging. These data show a broader chemokine binding repertoire of GPR182 than previously reported and they will be important for future work exploring the physiological and pathophysiological roles of GPR182, which we propose to be renamed atypical chemokine receptor 5 (ACKR5)

Fusion Scheme and Implementation Based on SRTM1, ASTER GDEM V3, and AW3D30

Multi-source data fusion can help to weaken the original data’s shortcomings while improving data accuracy. The experimental area in this research is Taiyuan City in Shanxi Province, China. Using SRTM1 DEM, ASTER GDEM V3, and AW3D30 DEM, the optimal resolution of the Fused DEM in the research area is determined by analyzing the topographic factor information entropy. Then the optimally weighted fusion coefficient of the DEM with root mean square error (RMSE) as the criterion under different slope classes is determined by traversal exploration and quantitatively evaluates the fusion effect. The results show that the optimal resolution of the Fused DEM is 40 m under the terrain feature constraint of Taiyuan city. The fused DEM decreases by 33.8%, 57.9%, and 11.5% for mean absolute error (MAE), 36.3%, 54.6%, and 1.4% for standard deviation (STD), and 32.8%, 54.2%, and 9.7% for root mean square error (RMSE) compared with SRTM1, ASTER GDEM V3, and AW3D30. The weighted average fusion of multiple intensities increased the accuracy of the original data. The reduced topographic factor errors, such as slope, profile curvature, and TPI, improved the Fused DEM’s topographic representation capacity. Furthermore, the results confirm the high accuracy of Fused DEM in complex mountainous regions

Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

Landslide susceptibility mapping is the first and most important step involved in landslide hazard assessment. The purpose of the present study is to compare three nonlinear approaches for landslide susceptibility mapping and test whether coal mining has a significant impact on landslide occurrence in coal mine areas. Landslide data collected by the Bureau of Land and Resources are represented by the X, Y coordinates of its central point; causative factors were calculated from topographic and geologic maps, as well as satellite imagery. The five-fold cross-validation method was adopted and the landslide/non-landslide datasets were randomly split into a ratio of 80:20. From this, five subsets for 20 times were acquired for training and validating models by GIS Geostatistical analysis methods, and all of the subsets were employed in a spatially balanced sample design. Three landslide models were built using support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models by selecting the median of the performance measures. Then, the three fitted models were compared using the area under the receiver operating characteristics (ROC) curves (AUC) and the performance measures. The results show that the prediction accuracies are between 73.43% and 87.45% in the training stage, and 67.16% to 73.13% in the validating stage for the three models. AUCs vary from 0.807 to 0.906 and 0.753 to 0.944 in the two stages, respectively. Additionally, three landslide susceptibility maps were obtained by classifying the range of landslide probabilities into four classes representing low (0–0.02), medium (0.02–0.1), high (0.1–0.85), and very high (0.85–1) probabilities of landslides. For the distributions of landslide and area percentages under different susceptibility standards, the SVM model has more relative balance in the four classes compared to the LR and the ANN models. The result reveals that the SVM model possesses better prediction efficiency than the other two models. Furthermore, the five factors, including lithology, distance from the road, slope angle, elevation, and land-use types, are the most suitable conditioning factors for landslide susceptibility mapping in the study area. The mining disturbance factor has little contribution to all models, because the mining method in this area is underground mining, so the mining depth is too deep to affect the stability of the slopes

Accuracy assessment of the ASTER GDEM and SRTM3 DEM: an example in the Loess Plateau and North China Plain of China

Chinese National Natural Science Fund 40871177 40830529The digital elevation model (DEM) produced by the Shuttle Radar Topographic Mission (SRTM) has provided important fundamental data for topographic analysis in many fields. The recently released global digital elevation model (GDEM) produced by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has higher spatial resolution and wider coverage than the SRTM3 DEM, and thus may be of more value to researchers. Taking two typical study areas-the Loess Plateau and the North China Plain of China-as an example, this article assesses the accuracy of the SRTM3 DEM and ASTER GDEM by collecting ground control points from topographical maps. It is found that both the SRTM3 DEM and the ASTER GDEM are far more accurate for the North China Plain than for the Loess Plateau. For the Loess Plateau, the accuracy of the ASTER GDEM is similar to that of the SRTM3 DEM; whereas for the North China Plain, it is much worse than that of the SRTM3 DEM. Considering the negative bias of the ASTER GDEM for flat or gentle regions, we improve its accuracy by adding the difference of the mean value between the SRTM3 DEM and ASTER GDEM for the North China Plain; then, the root mean square error (RMSE) of +/- 7.95 m from the original ASTER GDEM is improved to +/- 5.26 m, which demonstrates that it is a simple but useful way to improve the accuracy of the ASTER GDEM in flat or gentle regions

Combining Serum DNA Methylation Biomarkers and Protein Tumor Markers Improved Clinical Sensitivity for Early Detection of Colorectal Cancer

Background. Colorectal cancer (CRC) is one of the leading causes of cancer deaths worldwide and in China. Early CRC screening is the best approach to reduce its incidence and mortality rates. The ColoDefense test, a multiplex qPCR assay simultaneously detecting both methylated SEPT9 and SDC2 genes, has demonstrated improved clinical performance on either methylation biomarker alone for CRC screening with both blood and stool samples. Method. Leftover blood chemistry test samples from 125 CRC, 35 advanced adenoma, and 35 small polyp patients and 92 healthy control subjects were examined by the ColoDefense test. Among these samples, the levels of three circulating tumor markers, CEA, AFP, and CA19-9, were also measured for 106 CRC, 28 advanced adenoma, and 20 small polyp patients and all control subjects. Results. Due to the smaller volume and extended storage in nonfrozen state, the ColoDefense test with these samples exhibited reduced performance for all stages of CRC and advanced adenomas. The performance of CEA, AFP, and CA19-9 and their various combinations was also evaluated for CRC screening to identify the tumor marker combinations with the best performance. When combined with the ColoDefense test, the identified combinations did improve the clinical performance. Conclusion. These results suggested a rational path towards developing a CRC screening method that takes advantage of leftover blood chemistry test samples. The successful development of such a method will undoubtedly help promote early CRC screening by increasing its accessibility for the general public