Image_3_Construction of novel hypoxia-related gene model for prognosis and tumor microenvironment in endometrial carcinoma.tif

IntroductionEndometrial cancer is currently one of the three most common female reproductive cancers, which seriously threatens women’s lives and health. Hypoxia disrupts the tumor microenvironment, thereby affecting tumor progression and drug resistance.MethodsWe established hypoxia-related gene model to predict patient prognosis and 1-, 3-, and 5-year overall survival rates. Then, the expression level of hypoxia-related genes and survival data were extracted for comprehensive analysis by Cox regression analysis, and the model was established.ResultsWe analyzed the survival and prognosis of patients in the high and low-risk groups. The Kaplan-Meier curve showed that the low-risk group is associated with a better survival rate. The 1-, 3-, and 5-year AUC values of the model were 0.680, 0.698, and 0.687, respectively. Finally, we found that LAG3 may be a potential immune checkpoint for endometrial cancer.ConclusionWe found four hypoxia-related genes (ANXA2, AKAP12, NR3C1, and GPI) associated with prognosis. The hypoxia-related gene model can also predict prognosis and tumor microenvironment in endometrial cancer.</p

Table_1_Construction of novel hypoxia-related gene model for prognosis and tumor microenvironment in endometrial carcinoma.docx

IntroductionEndometrial cancer is currently one of the three most common female reproductive cancers, which seriously threatens women’s lives and health. Hypoxia disrupts the tumor microenvironment, thereby affecting tumor progression and drug resistance.MethodsWe established hypoxia-related gene model to predict patient prognosis and 1-, 3-, and 5-year overall survival rates. Then, the expression level of hypoxia-related genes and survival data were extracted for comprehensive analysis by Cox regression analysis, and the model was established.ResultsWe analyzed the survival and prognosis of patients in the high and low-risk groups. The Kaplan-Meier curve showed that the low-risk group is associated with a better survival rate. The 1-, 3-, and 5-year AUC values of the model were 0.680, 0.698, and 0.687, respectively. Finally, we found that LAG3 may be a potential immune checkpoint for endometrial cancer.ConclusionWe found four hypoxia-related genes (ANXA2, AKAP12, NR3C1, and GPI) associated with prognosis. The hypoxia-related gene model can also predict prognosis and tumor microenvironment in endometrial cancer.</p

Table_2_Construction of novel hypoxia-related gene model for prognosis and tumor microenvironment in endometrial carcinoma.docx

IntroductionEndometrial cancer is currently one of the three most common female reproductive cancers, which seriously threatens women’s lives and health. Hypoxia disrupts the tumor microenvironment, thereby affecting tumor progression and drug resistance.MethodsWe established hypoxia-related gene model to predict patient prognosis and 1-, 3-, and 5-year overall survival rates. Then, the expression level of hypoxia-related genes and survival data were extracted for comprehensive analysis by Cox regression analysis, and the model was established.ResultsWe analyzed the survival and prognosis of patients in the high and low-risk groups. The Kaplan-Meier curve showed that the low-risk group is associated with a better survival rate. The 1-, 3-, and 5-year AUC values of the model were 0.680, 0.698, and 0.687, respectively. Finally, we found that LAG3 may be a potential immune checkpoint for endometrial cancer.ConclusionWe found four hypoxia-related genes (ANXA2, AKAP12, NR3C1, and GPI) associated with prognosis. The hypoxia-related gene model can also predict prognosis and tumor microenvironment in endometrial cancer.</p

Image_1_Construction of novel hypoxia-related gene model for prognosis and tumor microenvironment in endometrial carcinoma.tif

IntroductionEndometrial cancer is currently one of the three most common female reproductive cancers, which seriously threatens women’s lives and health. Hypoxia disrupts the tumor microenvironment, thereby affecting tumor progression and drug resistance.MethodsWe established hypoxia-related gene model to predict patient prognosis and 1-, 3-, and 5-year overall survival rates. Then, the expression level of hypoxia-related genes and survival data were extracted for comprehensive analysis by Cox regression analysis, and the model was established.ResultsWe analyzed the survival and prognosis of patients in the high and low-risk groups. The Kaplan-Meier curve showed that the low-risk group is associated with a better survival rate. The 1-, 3-, and 5-year AUC values of the model were 0.680, 0.698, and 0.687, respectively. Finally, we found that LAG3 may be a potential immune checkpoint for endometrial cancer.ConclusionWe found four hypoxia-related genes (ANXA2, AKAP12, NR3C1, and GPI) associated with prognosis. The hypoxia-related gene model can also predict prognosis and tumor microenvironment in endometrial cancer.</p

Image_2_Construction of novel hypoxia-related gene model for prognosis and tumor microenvironment in endometrial carcinoma.tif

IntroductionEndometrial cancer is currently one of the three most common female reproductive cancers, which seriously threatens women’s lives and health. Hypoxia disrupts the tumor microenvironment, thereby affecting tumor progression and drug resistance.MethodsWe established hypoxia-related gene model to predict patient prognosis and 1-, 3-, and 5-year overall survival rates. Then, the expression level of hypoxia-related genes and survival data were extracted for comprehensive analysis by Cox regression analysis, and the model was established.ResultsWe analyzed the survival and prognosis of patients in the high and low-risk groups. The Kaplan-Meier curve showed that the low-risk group is associated with a better survival rate. The 1-, 3-, and 5-year AUC values of the model were 0.680, 0.698, and 0.687, respectively. Finally, we found that LAG3 may be a potential immune checkpoint for endometrial cancer.ConclusionWe found four hypoxia-related genes (ANXA2, AKAP12, NR3C1, and GPI) associated with prognosis. The hypoxia-related gene model can also predict prognosis and tumor microenvironment in endometrial cancer.</p

A correction technique for false topographic perception of remote-sensing images based on an inverse topographic correction technique

<p>The false topographic perception phenomenon (FTPP) refers to the visual misperception in remote-sensing images that certain types of terrains are visually interpreted as other types in rugged lands, for example, valleys as ridges and troughs as peaks. For this reason, the FTPP can influence the visualization and interpretation of images to a great extent. To scrutinize this problem, the paper firstly reviews and tests the existing FTPP-correction techniques and identifies the inverse slope-matching technique as an effective approach to visually enhance remote-sensing images and retain the colour information. The paper then proposes an improved FTPP-correction procedure that incorporates other image-processing techniques (e.g. linear stretch, histogram matching, and flat-area replacement) to enhance the performance of this technique. A further evaluation of the proposed technique is conducted by applying the technique to various study areas and using different types of remote-sensing images. The result indicates the method is relatively robust and will be a significant extension to geovisual analytics in digital earth research.</p

The distribution of <i>Q</i>. <i>mongolica</i> forests and the weather stations in Northeast China.

<p>The distribution of <i>Q</i>. <i>mongolica</i> forests and the weather stations in Northeast China.</p

The temporal changes in the green-up dates for <i>Q</i>. <i>mongolica</i> in Northeast China from 1962 to 2012.

<p>(A) The temporal changes in the green-up dates over 33 weather stations; (B) the frequency distribution of the temporal changes in green-up dates; and (C) the interannual variations in green-up dates (DOY) for the 33 stations and the change trend (days decade^-1). Note: the numbers in (A) indicate the advanced (minus number) or delayed (plus number) days at each weather station.</p

Change in the Green-Up Dates for <i>Quercus mongolica</i> in Northeast China and Its Climate-Driven Mechanism from 1962 to 2012

<div><p>The currently available studies on the green-up date were mainly based on ground observations and/or satellite data, and few model simulations integrated with wide coverage satellite data have been reported at large scale over a long time period (i.e., > 30 years). In this study, we combined phenology mechanism model, long-term climate data and synoptic scale remote sensing data to investigate the change in the green-up dates for <i>Quercus mongolica</i> over 33 weather stations in Northeast China and its climate-driven mechanism during 1962-2012. The results indicated that the unified phenology model can be well parameterized with the satellite derived green-up dates. The optimal daily mean temperature for chilling effect was between -27°C and 1°C for <i>Q</i>. <i>mongolica</i> in Northeast China, while the optimal daily mean temperature for forcing effect was above -3°C. The green-up dates for <i>Q</i>. <i>mongolica</i> across Northeast China showed a delayed latitudinal gradient of 2.699 days degree^-1, with the earliest date on the Julian day 93 (i.e., 3^th April) in the south and the latest date on the Julian day 129 (i.e., 9^th May) in the north. The green-up date for <i>Q</i>. <i>mongolica</i> in Northeast China has advanced 6.6 days (1.3 days decade^-1) from 1962 to 2012. With the prevailing warming in autumn, winter and spring in Northeast China during the past 51 years, the chilling effect for <i>Q</i>. <i>mongolica</i> has been weakened, while the forcing effect has been enhanced. The advancing trend in the green-up dates for <i>Q</i>. <i>mongolica</i> implied that the enhanced forcing effect to accelerate green-up was stronger than the weakened chilling effect to hold back green-up while the changes of both effects were caused by the warming climate.</p></div

The temporal mean green-up dates (DOY) for <i>Q</i>. <i>mongolica</i> in Northeast China during 1962–2012.

<p>(A) The spatial distribution of mean green-up dates over 33 weather stations; (B) the frequency distribution of green-up dates; and (C) the relationship between the green-up date and latitude. Note: the numbers in (A) indicate the green-up dates (DOY) at each weather station.</p