Accurate, updated information on the distribution of wetlands is essential for estimating net fluxes of greenhouse gases and for effectively protecting and managing wetlands. Because of their complex community structu...Accurate, updated information on the distribution of wetlands is essential for estimating net fluxes of greenhouse gases and for effectively protecting and managing wetlands. Because of their complex community structure and rich surface vegetation, deciduous broad-leaved forested swamps are considered to be one of the most difficult types of wetland to classify. In this research, with the support of remote sensing and geographic information system, multi-temporal radar images L-Palsar were used initially to extract the forest hydrological layer and phenology phase change layer as two variables through image analysis. Second, based on the environmental characteristics of forested swamps, three decision tree classifiers derived from the two variables were constructed to explore effective methods to identify deciduous broad-leaved forested swamps. Third, this study focused on analyzing the classification process between flat-forests, which are the most severely disturbed elements, and forested swamps. Finally, the application of the decision tree model will be discussed. The results showed that: 1) L-HH band(a L band with wavelength of 0–235 m in HH polarization mode; HH means Synthetic Aperture Radars transmit pulses in horizontal polarization and receive in horizontal polarization) in the leaf-off season is shown to be capable of detecting hydrologic conditions beneath the forest; 2) the accuracy of the classification(forested swamp and forest plat) was 81.5% based on hydrologic features, and 83.5% was achieved by combining hydrologic features and phenology response features, which indicated that hydrological characteristics under the forest played a key role. The HHOJ(refers to the band created by the subtraction with HH band in October and HH band in July) achieved by multi-temporal radar images did improve the classification accuracy, but not significantly, and more leaf-off radar images may be more efficient than multi-seasonal radar images for inland forested swamp mapping; 3) the lower separability between forested swamps dominated by vegetated surfaces and forest plat covered with litter was the main cause of the uncertainty in classification, which led to misleading interpretations of the pixel-based classification. Finally, through the analysis with kappa coefficients, it was shown that the value of the intersection point was an ideal choice for the variable.展开更多
目的:长时间漏气(prolonged air leak,PAL)是肺手术后最常见的术后并发症之一。这项研究旨在确定肺切除后PAL的风险因素,并构建一个术前预测模型,以估计其对单个患者的风险。方法:回顾性分析2017年01月至2021年06月期间接受解剖性肺切...目的:长时间漏气(prolonged air leak,PAL)是肺手术后最常见的术后并发症之一。这项研究旨在确定肺切除后PAL的风险因素,并构建一个术前预测模型,以估计其对单个患者的风险。方法:回顾性分析2017年01月至2021年06月期间接受解剖性肺切除的肺恶性肿瘤患者。PAL被定义为手术后7天以上的空气泄漏,并分析了风险因素。通过多因素Logistic回归模型,以识别独立的风险因素,并构建了列线图模型。采用重复抽样1000次的Bootstrap检验对列线图模型进行内部验证。采用一致性指数(concordance index,C-index)和校准曲线来表示模型的预测性能和预测准确度。决策曲线分析(decision curve analysis,DCA)评价该列线图模型的临床应用价值。结果:共有738名符合研究标准的患者纳入了这项研究。PAL的总体发病率为8.3%(61/738)。最终模型中包括身体质量指数(body mass index,BMI)、吸烟状况、手术时间、胸腔粘连和晚期肺癌炎症指数(advanced lung cancer inflammation index,ALI)。校准曲线表明该模型拟合较好;ROC曲线下面积(area under curve,AUC)为0.784(95%CI:0.720~0.848);DCA结果表明该模型具有较高的净获益水平。结论:本研究建立了列线图模型,对肺癌患者解剖性肺切除术后PAL的发生有较好的预测能力及良好的临床应用价值。展开更多
The unripe palmleaf raspberry,namely Fupenzi(FPZ),is an important medicinal and edible food.This study aims to evaluate the potential of FPZ extracts prepared with different approaches in attenuating hyperglycemia,gou...The unripe palmleaf raspberry,namely Fupenzi(FPZ),is an important medicinal and edible food.This study aims to evaluate the potential of FPZ extracts prepared with different approaches in attenuating hyperglycemia,gout,Alzheimer’s disease,and pigmentation,to obtain the enriching fraction and to identify the major active compounds.Results indicated that FPZ extracts showed weak activity against acetylcholinesterase,considerable ability against tyrosinase and xanthine oxidase,but excellent inhibition onα-glucosidase.Ultrasound-assisted 40%ethanol extract(40EUS)gave the highest phenolics content,and the bestα-glucosidase inhibition(IC_(50)=0.08μg/mL),which is 877-fold higher than that of positive control acarbose.The 40%ethanol eluting fraction of 40EUS showed the strongestα-glucosidase inhibition with the IC_(50) value of 37.79 ng/mL,it could also effectively attenuate the fasting blood glucose level and oral glucose tolerance of C57BL/6 mice.Twenty-six compounds were identified from 40%ethanol fraction by using HPLC-QTOF-MS/MS,hydrolysable tannins(including 11 ellagitannins and 4 gallotannins)were the major compounds,phenolic acids came to the second.Above results could provide important technical supporting for the further application and research of FPZ in health foods and drugs against diabetes.展开更多
基金Under the auspices of Special Funds of State Environmental Protection Public Welfare Industry(No.2011467032)
文摘Accurate, updated information on the distribution of wetlands is essential for estimating net fluxes of greenhouse gases and for effectively protecting and managing wetlands. Because of their complex community structure and rich surface vegetation, deciduous broad-leaved forested swamps are considered to be one of the most difficult types of wetland to classify. In this research, with the support of remote sensing and geographic information system, multi-temporal radar images L-Palsar were used initially to extract the forest hydrological layer and phenology phase change layer as two variables through image analysis. Second, based on the environmental characteristics of forested swamps, three decision tree classifiers derived from the two variables were constructed to explore effective methods to identify deciduous broad-leaved forested swamps. Third, this study focused on analyzing the classification process between flat-forests, which are the most severely disturbed elements, and forested swamps. Finally, the application of the decision tree model will be discussed. The results showed that: 1) L-HH band(a L band with wavelength of 0–235 m in HH polarization mode; HH means Synthetic Aperture Radars transmit pulses in horizontal polarization and receive in horizontal polarization) in the leaf-off season is shown to be capable of detecting hydrologic conditions beneath the forest; 2) the accuracy of the classification(forested swamp and forest plat) was 81.5% based on hydrologic features, and 83.5% was achieved by combining hydrologic features and phenology response features, which indicated that hydrological characteristics under the forest played a key role. The HHOJ(refers to the band created by the subtraction with HH band in October and HH band in July) achieved by multi-temporal radar images did improve the classification accuracy, but not significantly, and more leaf-off radar images may be more efficient than multi-seasonal radar images for inland forested swamp mapping; 3) the lower separability between forested swamps dominated by vegetated surfaces and forest plat covered with litter was the main cause of the uncertainty in classification, which led to misleading interpretations of the pixel-based classification. Finally, through the analysis with kappa coefficients, it was shown that the value of the intersection point was an ideal choice for the variable.
文摘目的:长时间漏气(prolonged air leak,PAL)是肺手术后最常见的术后并发症之一。这项研究旨在确定肺切除后PAL的风险因素,并构建一个术前预测模型,以估计其对单个患者的风险。方法:回顾性分析2017年01月至2021年06月期间接受解剖性肺切除的肺恶性肿瘤患者。PAL被定义为手术后7天以上的空气泄漏,并分析了风险因素。通过多因素Logistic回归模型,以识别独立的风险因素,并构建了列线图模型。采用重复抽样1000次的Bootstrap检验对列线图模型进行内部验证。采用一致性指数(concordance index,C-index)和校准曲线来表示模型的预测性能和预测准确度。决策曲线分析(decision curve analysis,DCA)评价该列线图模型的临床应用价值。结果:共有738名符合研究标准的患者纳入了这项研究。PAL的总体发病率为8.3%(61/738)。最终模型中包括身体质量指数(body mass index,BMI)、吸烟状况、手术时间、胸腔粘连和晚期肺癌炎症指数(advanced lung cancer inflammation index,ALI)。校准曲线表明该模型拟合较好;ROC曲线下面积(area under curve,AUC)为0.784(95%CI:0.720~0.848);DCA结果表明该模型具有较高的净获益水平。结论:本研究建立了列线图模型,对肺癌患者解剖性肺切除术后PAL的发生有较好的预测能力及良好的临床应用价值。
基金the financial support of National Natural Science Foundation of China(31860475)Key Youth Foundation of Jiangxi Province(20192ACB21011)Jiangxi“Shuangqian”Program(JXSQ2018101008).
文摘The unripe palmleaf raspberry,namely Fupenzi(FPZ),is an important medicinal and edible food.This study aims to evaluate the potential of FPZ extracts prepared with different approaches in attenuating hyperglycemia,gout,Alzheimer’s disease,and pigmentation,to obtain the enriching fraction and to identify the major active compounds.Results indicated that FPZ extracts showed weak activity against acetylcholinesterase,considerable ability against tyrosinase and xanthine oxidase,but excellent inhibition onα-glucosidase.Ultrasound-assisted 40%ethanol extract(40EUS)gave the highest phenolics content,and the bestα-glucosidase inhibition(IC_(50)=0.08μg/mL),which is 877-fold higher than that of positive control acarbose.The 40%ethanol eluting fraction of 40EUS showed the strongestα-glucosidase inhibition with the IC_(50) value of 37.79 ng/mL,it could also effectively attenuate the fasting blood glucose level and oral glucose tolerance of C57BL/6 mice.Twenty-six compounds were identified from 40%ethanol fraction by using HPLC-QTOF-MS/MS,hydrolysable tannins(including 11 ellagitannins and 4 gallotannins)were the major compounds,phenolic acids came to the second.Above results could provide important technical supporting for the further application and research of FPZ in health foods and drugs against diabetes.
