目的:多元线性回归模型在保持输入自变量光谱信息和空间特征的同时,通过线性变换获取自变量和因变量的光谱拟合关系,对原输入自变量的光谱信息进行优化,从而获得高空间分辨率和丰富光谱信息的重构数据。方法:利用同期获取的OLI(Operatio...目的:多元线性回归模型在保持输入自变量光谱信息和空间特征的同时,通过线性变换获取自变量和因变量的光谱拟合关系,对原输入自变量的光谱信息进行优化,从而获得高空间分辨率和丰富光谱信息的重构数据。方法:利用同期获取的OLI(Operational Land Imager)和PMS(Panchromatic and Multispectral Scanner)多光谱遥感影像,根据最小二乘法构建多元线性回归模型,重构生成具有丰富光谱特征和空间特征的遥感影像,从主客观两个方面评价重构影像的质量。结果:在目视解译(主观)方面,重构影像在一定程度上保留了原OLI影像的光谱特性,提升了原PMS影像的清晰度和分辨性;在量化角度(客观)方面,重构影像的信息量和平均梯度比原OLI对应波段影像的信息量(在部分波段上)和平均梯度要低,但比原PMS影像的信息量和平均梯度要高,可见重构影像的质量介于原PMS影像和OLI影像的质量之间。结论:以青海省门源回族自治县的耕地内不同作物为实例对象,利用最大似然法获取门源县青稞和油菜的空间分布,研究区实测数据验证表明,重构影像对耕地内部青稞与油菜的提取精度高于原PMS和OLI多光谱影像的提取精度。展开更多
Background: Pancreatic cancer is one of the most lethal types of cancer, and immunotherapy has become a promising remedy with advancements in tumor immunology. However, predicting the clinical response to immunotherap...Background: Pancreatic cancer is one of the most lethal types of cancer, and immunotherapy has become a promising remedy with advancements in tumor immunology. However, predicting the clinical response to immunotherapy in pancreatic cancer remains a dilemma for clinicians. Methods: GEPIA database was used to analyze the differential expression of MMR and PD-L1 genes in 33 common cancer types including pancreatic cancer. The expression levels of MMR and PD-L1 genes were downloaded from the GEPIA and GEO databases to analyze the correlation between MMR genes and PD-L1, and the clinicopathological and survival information were downloaded from the TCGA databases to analyze the relationship between the expression of MMR, PD-L1 and clinicopathological characteristics, prognosis. Meanwhile, the tumor tissue samples of 41 patients with pancreatic cancer were collected, and the protein expression levels of MMR and PD-L1 were detected by immunohistochemical assay. Furthermore, we analyzed the correlation between MMR and PD-L1, and the correlation between the expression of MMR, PD-L1 and clinicopathological characteristics, prognosis of pancreatic cancer patients. Results: Bioinformatics analysis showed that MLH1, MLH3, MSH2, MSH3, and PMS2 were highly expressed in most cancer types including pancreatic cancer (P P = 0.012), clinical stage (I vs II: P = 0.016), MSH2 expression was related to clinical stage (P < 0.05), T stage (T3 vs T4: P = 0.039), and MSH3 expression was related to T stage (P < 0.05). Besides, both MSH2 expression (P P = 0.044) were significantly associated with prognosis. GEPIA data also showed that MSH2 expression was related to prognosis (P = 0.008). The correlation analysis revealed that the expressions MSH2, MLH1, PMS2 had strong correlations with PD-L1 both in GEPIA and GEO databases. Real-world data indicated that of the 41 pancreatic cancer patients, 5 cases had MLH1 deletion, 5 cases had MSH2 deletion, 4 cases had PMS2 deletion, and 12 cases had PD-L1 positive expression. Notably, PMS2 deletion was associated with PD-L1 positive expression (P = 0.035). In addition, MLH1 was related to clinical stage (P = 0.033), age (P = 0.048), and MSH2 was related to clinical stage (P = 0.033). However, MLH1 (P = 0.697), MSH2 (P = 0.956), PMS2 (P = 0.341), and PD-L1 (P = 0.734) appeared to have no impact on overall survival among patients with pancreatic cancer. Conclusion: Both bioinformatics and real-world data showed that there were correlation between PMS2 deletion and PD-L1 expression, and correlation between MLH1, MSH2 and clinical stage.展开更多
文摘目的:多元线性回归模型在保持输入自变量光谱信息和空间特征的同时,通过线性变换获取自变量和因变量的光谱拟合关系,对原输入自变量的光谱信息进行优化,从而获得高空间分辨率和丰富光谱信息的重构数据。方法:利用同期获取的OLI(Operational Land Imager)和PMS(Panchromatic and Multispectral Scanner)多光谱遥感影像,根据最小二乘法构建多元线性回归模型,重构生成具有丰富光谱特征和空间特征的遥感影像,从主客观两个方面评价重构影像的质量。结果:在目视解译(主观)方面,重构影像在一定程度上保留了原OLI影像的光谱特性,提升了原PMS影像的清晰度和分辨性;在量化角度(客观)方面,重构影像的信息量和平均梯度比原OLI对应波段影像的信息量(在部分波段上)和平均梯度要低,但比原PMS影像的信息量和平均梯度要高,可见重构影像的质量介于原PMS影像和OLI影像的质量之间。结论:以青海省门源回族自治县的耕地内不同作物为实例对象,利用最大似然法获取门源县青稞和油菜的空间分布,研究区实测数据验证表明,重构影像对耕地内部青稞与油菜的提取精度高于原PMS和OLI多光谱影像的提取精度。
文摘Background: Pancreatic cancer is one of the most lethal types of cancer, and immunotherapy has become a promising remedy with advancements in tumor immunology. However, predicting the clinical response to immunotherapy in pancreatic cancer remains a dilemma for clinicians. Methods: GEPIA database was used to analyze the differential expression of MMR and PD-L1 genes in 33 common cancer types including pancreatic cancer. The expression levels of MMR and PD-L1 genes were downloaded from the GEPIA and GEO databases to analyze the correlation between MMR genes and PD-L1, and the clinicopathological and survival information were downloaded from the TCGA databases to analyze the relationship between the expression of MMR, PD-L1 and clinicopathological characteristics, prognosis. Meanwhile, the tumor tissue samples of 41 patients with pancreatic cancer were collected, and the protein expression levels of MMR and PD-L1 were detected by immunohistochemical assay. Furthermore, we analyzed the correlation between MMR and PD-L1, and the correlation between the expression of MMR, PD-L1 and clinicopathological characteristics, prognosis of pancreatic cancer patients. Results: Bioinformatics analysis showed that MLH1, MLH3, MSH2, MSH3, and PMS2 were highly expressed in most cancer types including pancreatic cancer (P P = 0.012), clinical stage (I vs II: P = 0.016), MSH2 expression was related to clinical stage (P < 0.05), T stage (T3 vs T4: P = 0.039), and MSH3 expression was related to T stage (P < 0.05). Besides, both MSH2 expression (P P = 0.044) were significantly associated with prognosis. GEPIA data also showed that MSH2 expression was related to prognosis (P = 0.008). The correlation analysis revealed that the expressions MSH2, MLH1, PMS2 had strong correlations with PD-L1 both in GEPIA and GEO databases. Real-world data indicated that of the 41 pancreatic cancer patients, 5 cases had MLH1 deletion, 5 cases had MSH2 deletion, 4 cases had PMS2 deletion, and 12 cases had PD-L1 positive expression. Notably, PMS2 deletion was associated with PD-L1 positive expression (P = 0.035). In addition, MLH1 was related to clinical stage (P = 0.033), age (P = 0.048), and MSH2 was related to clinical stage (P = 0.033). However, MLH1 (P = 0.697), MSH2 (P = 0.956), PMS2 (P = 0.341), and PD-L1 (P = 0.734) appeared to have no impact on overall survival among patients with pancreatic cancer. Conclusion: Both bioinformatics and real-world data showed that there were correlation between PMS2 deletion and PD-L1 expression, and correlation between MLH1, MSH2 and clinical stage.
