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In recent years, the red turpentine beetle (RTB) (Dendroctonus valens LeConte) has invaded the northern regions of China. Due to the short invasion time, the outbreak of tree mortality corresponded to a low level of damage. Important information about tree mortality, provided by remote sensing at both single-tree and forest stand scale, is needed in forest management at the early stages of outbreak. In order to detect RTB-induced tree mortality at a single-tree scale, we evaluated the classification accuracies of Gaofen-2 (GF2) imagery at different spatial resolutions (1 and 4 m) using a pixel-based method. We also simultaneously applied an object-based method to 1 m pan-sharpened images. We used Sentinel-2 (S2) imagery with different resolutions (10 and 20 m) to detect RTB-induced tree mortality and compared their classification accuracies at a larger scale—the stand scale. Three kinds of machine learning algorithms—the classification and regression tree (CART), the random forest (RF), and the support vector machine (SVM)—were applied and compared in this study. The results showed that 1 m resolution GF2 images had the highest classification accuracy using the pixel-based method and SVM algorithm (overall accuracy = 77.7%). We found that the classification of three degrees of damage percentage within the S2 pixel (0%, <15%, and 15% < x < 50%) was not successful at a forest stand scale. However, 10 m resolution S2 images could acquire effective binary classification (<15%: overall accuracy = 74.9%; 15% < x < 50%: overall accuracy = 81.0%). Our results indicated that identifying tree mortality caused by RTB at a single-tree and forest stand scale was accomplished with the combination of GF2 and S2 images. Our results are very useful for the future exploration of the patterns of spatial and temporal changes in insect pest transmission at different spatial scales. [ABSTRACT FROM AUTHOR]

Objective: To understand the situations of departments of intensive care units (ICUs) of different level hospitals in Guizhou Province, and to provide directions and evidences for improving quality control in critical care medicine., Methods: A county-level hospital and a provincial-level hospital's comprehensive ICU in Guizhou Province were selected to record and analyze and compare the structural indicators, patient admission and transfer, disease distribution, ventilator associated pneumonia (VAP), intravascular catheter related blood stream infection (CRBSI) and catheter-associated urinary tract infection (CAUTI) of the two hospitals' comprehensive ICU in 2017., Results: The ICU of the People's Hospital of Suiyang County (county hospital) was found in 2012, and the ICU of the Affiliated Hospital of Guizhou Medical University (provincial hospital) was found in 1994. Until 2017, there were 10 and 46 beds, 6 (all of them hold bachelor's degree) and 18 physicians (6 of them hold PhD, 5 of them hold master's degree, 7 of them hold bachelor's degree), 17 (4 of them hold bachelor's degree, 13 of them hold college degree or graduated from secondary school) and 69 nurses (2 of them hold master's degree, 53 of them hold bachelor's degree, 14 of them hold college degree or graduated from secondary school) in the two ICUs respectively, there were significant differences in the education background of the physicians and nurses between the two ICUs (both P < 0.01). During 2017, 471 cases were admitted to the ICU of the county hospital while 1 633 cases were admitted to the ICU of the provincial hospital. Compared with the ICU of the provincial hospital, the ratio of the patients with acute physiology and chronic health evaluation II (APACHE II) ≥ 15 at admission was lower (74.8% vs. 85.1%, P < 0.01), the ratio of direct admission was higher (30.8% vs. 17.4%, P < 0.01), the ratio of the patients admitted to the ICU more than once was lower (0.8% vs. 5.0%, P < 0.01), the ratio of the patients whose the length of ICU stay less than 24 hours was higher (51.6% vs. 13.7%, P < 0.01), the ratio of the patients whose the length of ICU stay more than 28 days was lower (1.1% vs. 2.9%, P < 0.05), the ratio of the patients discharged against-advice (25.5% vs. 20.5%, P < 0.05) was higher, the ratio of the patients transferred to other hospitals was higher (5.1% vs. 0.3%, P < 0.05), and the ICU mortality was lower (4.0% vs. 13.9%, P < 0.01) in the ICU of the county hospital. The top three kinds of diseases treated in the ICU of the county hospital were brain injury (27.4%), trauma (19.1%) and toxication (6.8%); while in the ICU of the provincial hospital were brain injury (18.6%), sepsis (16.2%) and severe acute pancreatitis (4.8%). In addition, the incidences of VAP, CRBSI and CAUTI in the ICU of the county hospital were 10.0/1 000 ventilator days, 1.4/1 000 catheter days, 0.5/1 000 catheter days; while in the ICU of the provincial hospital were 5.8/1 000 ventilator days, 2.0/1 000 catheter days, 3.7/1 000 catheter days, respectively., Conclusions: There are short of physicians and nurses in the ICU of the provincial and county hospitals in Guizhou Province, and the educational level of the medical staff in the ICU of the county hospital is relatively low. Moreover, there were significant differences in the admissions and treatments and the outcomes of the critically ill patients between the two ICUs. The characteristics of the ICUs of county hospitals should be fully considered when the quality control of critical care medicine and continuing medical education are done.