Environmental covariates are the basis of predictive soil mapping.Their selection determines the performance of soil mapping to a great extent,especially in cases where the number of soil samples is limited but soil s...Environmental covariates are the basis of predictive soil mapping.Their selection determines the performance of soil mapping to a great extent,especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high.In this study,we proposed an integrated method to select environmental covariates for predictive soil depth mapping.First,candidate variables that may influence the development of soil depth were selected based on pedogenetic knowledge.Second,three conventional methods(Pearson correlation analysis(PsCA),generalized additive models(GAMs),and Random Forest(RF))were used to generate optimal combinations of environmental covariates.Finally,three optimal combinations were integrated to produce a final combination based on the importance and occurrence frequency of each environmental covariate.We tested this method for soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China.A total of 129 soil sampling sites were collected using a representative sampling strategy,and RF and support vector machine(SVM)models were used to map soil depth.The results showed that compared to the set of environmental covariates selected by the three conventional selection methods,the set of environmental covariates selected by the proposed method achieved higher mapping accuracy.The combination from the proposed method obtained a root mean square error(RMSE)of 11.88 cm,which was 2.25–7.64 cm lower than the other methods,and an R^2 value of 0.76,which was 0.08–0.26 higher than the other methods.The results suggest that our method can be used as an alternative to the conventional methods for soil depth mapping and may also be effective for mapping other soil properties.展开更多
Understanding the soil taxonomy and distribution characteristics of the permafrost region in the Qinghai-Tibet Plateau(QTP) is very important. On the basis of extensive field surveys and experimental analysis, this st...Understanding the soil taxonomy and distribution characteristics of the permafrost region in the Qinghai-Tibet Plateau(QTP) is very important. On the basis of extensive field surveys and experimental analysis, this study carries out soil taxonomic classification of the permafrost region in the QTP. According to Chinese Soil Taxonomy, the soil of the permafrost region in the QTP can be divided into 6 Orders(Histosols, Aridosols, Gleyosols, Isohumosols, Cambosols, Primosols), 11 Suborders, 19 Groups and 24 Subgroups. Cambosols are the dominant soil type in the permafrost region, followed by Aridosols. From the east to the west of the permafrost region in the QTP, the soil type gradually changes from Cambosols to Aridosols, showing a meridional zonality. The eastern region is dominated by Cambosols, with no obvious latitudinal zonality. From the south to the northwest of the western region, the dominance of Aridosols and Cambosols gradually transited to Aridosols, presenting a latitudinal zonality. The soil in the western region shows a poor vertical zonality, while the distribution of suborders of Cambosols in the eastern region shows a more obvious vertical zonality. The result indicates that precipitation and vegetation are the main factors that influence the zonal distribution of soil. The permafrost in the east has some effect on the vertical soil zonality, but the effect is weakened in the west.展开更多
Soil depth is critical for eco-hydrological modeling,carbon storage calculation and land evaluation.However,its spatial variation is poorly understood and rarely mapped.With a limited number of sparse samples,how to p...Soil depth is critical for eco-hydrological modeling,carbon storage calculation and land evaluation.However,its spatial variation is poorly understood and rarely mapped.With a limited number of sparse samples,how to predict soil depth in a large area of complex landscapes is still an issue.This study constructed an ensemble machine learning model,i.e.,quantile regression forest,to quantify the relationship between soil depth and environmental conditions.The model was then combined with a rich set of environmental covariates to predict spatial variation of soil depth and straightforwardly estimate the associated predictive uncertainty in the 140000 km^(2)Heihe River basin of northwestern China.A total of 275 soil depth observation points and 26 covariates were used.The results showed a model predictive accuracy with coefficient of determination(R)of 0.587 and root mean square error(RMSE)of 2.98 cm(square root scale),i.e.,almost 60% of soil depth variation explained.The resulting soil depth map clearly exhibited regional patterns as well as local details.Relatively deep soils occurred in low lying landscape positions such as valley bottoms and plains while shallow soils occurred in high and steep landscape positions such as hillslopes,ridges and terraces.The oases had much deeper soils than outside semi-desert areas,the middle of an alluvial plain had deeper soils than its margins,and the middle of a lacustrine plain had shallower soils than its margins.Large predictive uncertainty mainly occurred in areas with a lack of soil survey points.Both pedogenic and geomorphic processes contributed to the shaping of soil depth pattern of this basin but the latter was dominant.This findings may be applicable to other similar basins in cold and arid regions around the world.展开更多
目的观察胃癌患者血浆配对盒蛋白5(paired box protein 5,PAX5)与环指蛋白180(ring finger protein 180,RNF180)基因甲基化情况,探讨其与临床病理特征及预后的关系。方法110例胃癌患者为胃癌组,同期体检健康者50例为对照组,采用甲基化...目的观察胃癌患者血浆配对盒蛋白5(paired box protein 5,PAX5)与环指蛋白180(ring finger protein 180,RNF180)基因甲基化情况,探讨其与临床病理特征及预后的关系。方法110例胃癌患者为胃癌组,同期体检健康者50例为对照组,采用甲基化特异性PCR法检测血浆PAX5与RNF180基因甲基化状态,分析其与胃癌患者临床病理特征的关系。随访3年,采用Kaplan-Meier生存曲线分析PAX5、RNF180甲基化与患者预后的关系。结果胃癌患者血浆PAX5、RNF180基因启动子区甲基化发生率(70.00%、62.73%)高于对照组(32.00%、30.00%)(P<0.05),胃癌组TNM分期Ⅰ~Ⅱ期患者血浆PAX5、RNF180基因甲基化发生率(65.17%、57.30%)低于TNM分期Ⅲ~Ⅳ期患者(90.48%、85.71%)(P<0.05),胃癌组男性与女性、年龄<55岁与≥55岁、肿瘤直径<2.5 cm与≥2.5 cm、细胞低-中分化与高分化及有无淋巴结转移患者血浆PAX5、RNF180基因甲基化发生率比较差异均无统计学意义(P>0.05)。发生PAX5基因甲基化的胃癌患者3年生存率(68.83%)低于未发生甲基化患者(84.85%)(P<0.05),发生RNF180基因甲基化的胃癌患者3年总体生存率(73.91%)与未发生甲基化胃癌患者(73.17%)比较差异无统计学意义(P>0.05)。结论胃癌患者血浆PAX5与RNF180基因甲基化发生率增高,与肿瘤TNM分期有关,发生PAX5基因甲基化的患者3年总生存率低。展开更多
[目的]比较开放复位内固定与闭合复位外固定治疗桡骨远端C2、C3型骨折的临床效果。[方法]回顾性分析2015年5月~2019年6月本院收治的C2、C3型桡骨远端骨折患者120例,依据术前医患沟通结果,62例采用开放内固定治疗,58例采用闭合复位外固...[目的]比较开放复位内固定与闭合复位外固定治疗桡骨远端C2、C3型骨折的临床效果。[方法]回顾性分析2015年5月~2019年6月本院收治的C2、C3型桡骨远端骨折患者120例,依据术前医患沟通结果,62例采用开放内固定治疗,58例采用闭合复位外固定术治疗。比较两组患者的围手术期、随访和影像资料。[结果]两组均顺利完成手术。内固定组患者的手术时间及术中出血量均明显多于外固定组,而内固定组的术中透视次数、住院时间显著少于外固定组(P<0.05)。所有患者随访12~29个月,平均(14.52±3.41)个月。内固定组恢复完全负重活动时间显著少于外固定组[(26.89±4.97)周vs(38.54±5.37)周,P<0.05]。随术后时间推移,两组患者VAS评分和Gartland-Werley评分均显著减少(P<0.05),而两组腕背伸-掌屈和尺偏-桡偏活动度均显著增加(P<0.05);相应时间点,内固定组的上述指标均优于外固定组(P<0.05)。影像方面,末次随访时,内固定组掌倾角[(11.13±2.82)°vs (7.53±2.78)°,P<0.05]、尺偏角[(23.47±2.63)°vs (16.45±2.84)°,P<0.05]及桡骨高度[(13.73±1.86) mm vs (10.76±1.85) mm,P<0.05]均显著优于外固定组。术后影像显示,内固定组关节面复位质量显著优于外固定组(P<0.05)。内固定组骨折愈合时间显著早于外固定组(P<0.05)。[结论]开放复位内固定治疗桡骨远端C2、C3型骨折的临床效果显著优于闭合复位外固定。展开更多
The use of landscape covariates to estimate soil properties is not suitable for the areas of low relief due to the high variability of soil properties in similar topographic and vegetation conditions.A new method was ...The use of landscape covariates to estimate soil properties is not suitable for the areas of low relief due to the high variability of soil properties in similar topographic and vegetation conditions.A new method was implemented to map regional soil texture (in terms of sand,silt and clay contents) by hypothesizing that the change in the land surface diurnal temperature difference (DTD) is related to soil texture in case of a relatively homogeneous rainfall input.To examine this hypothesis,the DTDs from moderate resolution imagine spectroradiometer (MODIS) during a selected time period,i.e.,after a heavy rainfall between autumn harvest and autumn sowing,were classified using fuzzy-c-means (FCM) clustering.Six classes were generated,and for each class,the sand (> 0.05 mm),silt (0.002-0.05 mm) and clay (< 0.002 mm) contents at the location of maximum membership value were considered as the typical values of that class.A weighted average model was then used to digitally map soil texture.The results showed that the predicted map quite accurately reflected the regional soil variation.A validation dataset produced estimates of error for the predicted maps of sand,silt and clay contents at root mean of squared error values of 8.4%,7.8% and 2.3%,respectively,which is satisfactory in a practical context.This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.展开更多
Based on legacy soil data from a soil survey conducted recently in the traditional manner in Hong Kong of China, a digital soil mapping method was applied to produce soil order information for mountain areas of Hong K...Based on legacy soil data from a soil survey conducted recently in the traditional manner in Hong Kong of China, a digital soil mapping method was applied to produce soil order information for mountain areas of Hong Kong. Two modeling methods (decision tree analysis and linear discriminant analysis) were used, and their applications were compared. Much more eflort was put on selecting soil covariates for modeling. First, analysis of variance (ANOVA) was used to test the variance of terrain attributes between soil orders. Then, a stepwise procedure was used to select soil covariates for linear discriminant analysis, and a backward removing procedure was developed to select soil covariates for tree modeling. At the same time, ANOVA results, as well as our knowledge and experience on soil mapping, were also taken into account for selecting soil covariates for tree modeling. Two linear discriminant models and four tree models were established finally, and their prediction performances were validated using a multiple jackknifing approach. Results showed that the discriminant model built on ANOVA results performed best, followed by the discriminant model built by stepwise, the tree model built by the backward removing procedure, the tree model built according to knowledge and experience on soil mapping, and the tree model built automatically. The results highlighted the importance of selecting soil covariates in modeling for soil mapping, and suggested the usefulness of methods used in this study for selecting soil covariates. The best discriminant model was finally selected to map soil orders for this area, and validation results showed that thus produced soil order map had a high accuracy.展开更多
基金supported financially by the National Natural Science Foundation of China (91325301, 41571212 and 41137224)the Project of "One-Three-Five" Strategic Planning & Frontier Sciences of the Institute of Soil Science, Chinese Academy of Sciences (ISSASIP1622)the National Key Basic Research Special Foundation of China (2012FY112100)
文摘Environmental covariates are the basis of predictive soil mapping.Their selection determines the performance of soil mapping to a great extent,especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high.In this study,we proposed an integrated method to select environmental covariates for predictive soil depth mapping.First,candidate variables that may influence the development of soil depth were selected based on pedogenetic knowledge.Second,three conventional methods(Pearson correlation analysis(PsCA),generalized additive models(GAMs),and Random Forest(RF))were used to generate optimal combinations of environmental covariates.Finally,three optimal combinations were integrated to produce a final combination based on the importance and occurrence frequency of each environmental covariate.We tested this method for soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China.A total of 129 soil sampling sites were collected using a representative sampling strategy,and RF and support vector machine(SVM)models were used to map soil depth.The results showed that compared to the set of environmental covariates selected by the three conventional selection methods,the set of environmental covariates selected by the proposed method achieved higher mapping accuracy.The combination from the proposed method obtained a root mean square error(RMSE)of 11.88 cm,which was 2.25–7.64 cm lower than the other methods,and an R^2 value of 0.76,which was 0.08–0.26 higher than the other methods.The results suggest that our method can be used as an alternative to the conventional methods for soil depth mapping and may also be effective for mapping other soil properties.
基金financially supported by the National Major Scientific Project of China "Cryospheric Change and Impacts Research" program "Research of permafrost hydrothermal process and its response to climate change" (Grant No. 2013CBA01803)Chinese Academy of Sciences (KJZD-EW-G03-02)
文摘Understanding the soil taxonomy and distribution characteristics of the permafrost region in the Qinghai-Tibet Plateau(QTP) is very important. On the basis of extensive field surveys and experimental analysis, this study carries out soil taxonomic classification of the permafrost region in the QTP. According to Chinese Soil Taxonomy, the soil of the permafrost region in the QTP can be divided into 6 Orders(Histosols, Aridosols, Gleyosols, Isohumosols, Cambosols, Primosols), 11 Suborders, 19 Groups and 24 Subgroups. Cambosols are the dominant soil type in the permafrost region, followed by Aridosols. From the east to the west of the permafrost region in the QTP, the soil type gradually changes from Cambosols to Aridosols, showing a meridional zonality. The eastern region is dominated by Cambosols, with no obvious latitudinal zonality. From the south to the northwest of the western region, the dominance of Aridosols and Cambosols gradually transited to Aridosols, presenting a latitudinal zonality. The soil in the western region shows a poor vertical zonality, while the distribution of suborders of Cambosols in the eastern region shows a more obvious vertical zonality. The result indicates that precipitation and vegetation are the main factors that influence the zonal distribution of soil. The permafrost in the east has some effect on the vertical soil zonality, but the effect is weakened in the west.
基金supported by the National Natural Science Foundation of China(41130530,91325301 and 42071072)。
文摘Soil depth is critical for eco-hydrological modeling,carbon storage calculation and land evaluation.However,its spatial variation is poorly understood and rarely mapped.With a limited number of sparse samples,how to predict soil depth in a large area of complex landscapes is still an issue.This study constructed an ensemble machine learning model,i.e.,quantile regression forest,to quantify the relationship between soil depth and environmental conditions.The model was then combined with a rich set of environmental covariates to predict spatial variation of soil depth and straightforwardly estimate the associated predictive uncertainty in the 140000 km^(2)Heihe River basin of northwestern China.A total of 275 soil depth observation points and 26 covariates were used.The results showed a model predictive accuracy with coefficient of determination(R)of 0.587 and root mean square error(RMSE)of 2.98 cm(square root scale),i.e.,almost 60% of soil depth variation explained.The resulting soil depth map clearly exhibited regional patterns as well as local details.Relatively deep soils occurred in low lying landscape positions such as valley bottoms and plains while shallow soils occurred in high and steep landscape positions such as hillslopes,ridges and terraces.The oases had much deeper soils than outside semi-desert areas,the middle of an alluvial plain had deeper soils than its margins,and the middle of a lacustrine plain had shallower soils than its margins.Large predictive uncertainty mainly occurred in areas with a lack of soil survey points.Both pedogenic and geomorphic processes contributed to the shaping of soil depth pattern of this basin but the latter was dominant.This findings may be applicable to other similar basins in cold and arid regions around the world.
文摘目的观察胃癌患者血浆配对盒蛋白5(paired box protein 5,PAX5)与环指蛋白180(ring finger protein 180,RNF180)基因甲基化情况,探讨其与临床病理特征及预后的关系。方法110例胃癌患者为胃癌组,同期体检健康者50例为对照组,采用甲基化特异性PCR法检测血浆PAX5与RNF180基因甲基化状态,分析其与胃癌患者临床病理特征的关系。随访3年,采用Kaplan-Meier生存曲线分析PAX5、RNF180甲基化与患者预后的关系。结果胃癌患者血浆PAX5、RNF180基因启动子区甲基化发生率(70.00%、62.73%)高于对照组(32.00%、30.00%)(P<0.05),胃癌组TNM分期Ⅰ~Ⅱ期患者血浆PAX5、RNF180基因甲基化发生率(65.17%、57.30%)低于TNM分期Ⅲ~Ⅳ期患者(90.48%、85.71%)(P<0.05),胃癌组男性与女性、年龄<55岁与≥55岁、肿瘤直径<2.5 cm与≥2.5 cm、细胞低-中分化与高分化及有无淋巴结转移患者血浆PAX5、RNF180基因甲基化发生率比较差异均无统计学意义(P>0.05)。发生PAX5基因甲基化的胃癌患者3年生存率(68.83%)低于未发生甲基化患者(84.85%)(P<0.05),发生RNF180基因甲基化的胃癌患者3年总体生存率(73.91%)与未发生甲基化胃癌患者(73.17%)比较差异无统计学意义(P>0.05)。结论胃癌患者血浆PAX5与RNF180基因甲基化发生率增高,与肿瘤TNM分期有关,发生PAX5基因甲基化的患者3年总生存率低。
文摘[目的]比较开放复位内固定与闭合复位外固定治疗桡骨远端C2、C3型骨折的临床效果。[方法]回顾性分析2015年5月~2019年6月本院收治的C2、C3型桡骨远端骨折患者120例,依据术前医患沟通结果,62例采用开放内固定治疗,58例采用闭合复位外固定术治疗。比较两组患者的围手术期、随访和影像资料。[结果]两组均顺利完成手术。内固定组患者的手术时间及术中出血量均明显多于外固定组,而内固定组的术中透视次数、住院时间显著少于外固定组(P<0.05)。所有患者随访12~29个月,平均(14.52±3.41)个月。内固定组恢复完全负重活动时间显著少于外固定组[(26.89±4.97)周vs(38.54±5.37)周,P<0.05]。随术后时间推移,两组患者VAS评分和Gartland-Werley评分均显著减少(P<0.05),而两组腕背伸-掌屈和尺偏-桡偏活动度均显著增加(P<0.05);相应时间点,内固定组的上述指标均优于外固定组(P<0.05)。影像方面,末次随访时,内固定组掌倾角[(11.13±2.82)°vs (7.53±2.78)°,P<0.05]、尺偏角[(23.47±2.63)°vs (16.45±2.84)°,P<0.05]及桡骨高度[(13.73±1.86) mm vs (10.76±1.85) mm,P<0.05]均显著优于外固定组。术后影像显示,内固定组关节面复位质量显著优于外固定组(P<0.05)。内固定组骨折愈合时间显著早于外固定组(P<0.05)。[结论]开放复位内固定治疗桡骨远端C2、C3型骨折的临床效果显著优于闭合复位外固定。
基金Supported by the Basic Research Program of Jiangsu Province,China (No. BK2008058)the Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX2-YW-409)
文摘The use of landscape covariates to estimate soil properties is not suitable for the areas of low relief due to the high variability of soil properties in similar topographic and vegetation conditions.A new method was implemented to map regional soil texture (in terms of sand,silt and clay contents) by hypothesizing that the change in the land surface diurnal temperature difference (DTD) is related to soil texture in case of a relatively homogeneous rainfall input.To examine this hypothesis,the DTDs from moderate resolution imagine spectroradiometer (MODIS) during a selected time period,i.e.,after a heavy rainfall between autumn harvest and autumn sowing,were classified using fuzzy-c-means (FCM) clustering.Six classes were generated,and for each class,the sand (> 0.05 mm),silt (0.002-0.05 mm) and clay (< 0.002 mm) contents at the location of maximum membership value were considered as the typical values of that class.A weighted average model was then used to digitally map soil texture.The results showed that the predicted map quite accurately reflected the regional soil variation.A validation dataset produced estimates of error for the predicted maps of sand,silt and clay contents at root mean of squared error values of 8.4%,7.8% and 2.3%,respectively,which is satisfactory in a practical context.This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.
基金Supported by the Public Policy Research of the Research Grants Council of Hong Kong, China (No.2002-PPR-3)the Knowledge Innovation Program of the Chinese Academy of Sciences (No.KZCX2-YW-409)+1 种基金the National Natural ScienceFoundation of China (Nos.40625001 and 40771092)the Mini-AOE (Area of Excellence) Fund from the Hong Kong Baptist University,China (No.RC/AOE/08-09/01)
文摘Based on legacy soil data from a soil survey conducted recently in the traditional manner in Hong Kong of China, a digital soil mapping method was applied to produce soil order information for mountain areas of Hong Kong. Two modeling methods (decision tree analysis and linear discriminant analysis) were used, and their applications were compared. Much more eflort was put on selecting soil covariates for modeling. First, analysis of variance (ANOVA) was used to test the variance of terrain attributes between soil orders. Then, a stepwise procedure was used to select soil covariates for linear discriminant analysis, and a backward removing procedure was developed to select soil covariates for tree modeling. At the same time, ANOVA results, as well as our knowledge and experience on soil mapping, were also taken into account for selecting soil covariates for tree modeling. Two linear discriminant models and four tree models were established finally, and their prediction performances were validated using a multiple jackknifing approach. Results showed that the discriminant model built on ANOVA results performed best, followed by the discriminant model built by stepwise, the tree model built by the backward removing procedure, the tree model built according to knowledge and experience on soil mapping, and the tree model built automatically. The results highlighted the importance of selecting soil covariates in modeling for soil mapping, and suggested the usefulness of methods used in this study for selecting soil covariates. The best discriminant model was finally selected to map soil orders for this area, and validation results showed that thus produced soil order map had a high accuracy.
