To better understand the physiological and biochemical mechanisms of waterlogging tolerance, waterlogging effects on lipid peroxidation and the activity of antioxidative enzymes were investigated in leaves and roots o...To better understand the physiological and biochemical mechanisms of waterlogging tolerance, waterlogging effects on lipid peroxidation and the activity of antioxidative enzymes were investigated in leaves and roots of two maize genotypes, HZ32 （waterlogging-tolerant） and K12 （waterlogging-sensitive）. Potted maize plants were waterlogged at the second leaf stage under glasshouse conditions. Leaves and roots were harvested 1 d before and 2, 4, 6, 8 and 10 d after the start of waterlogging treatment. Through comparing the activities of superoxide dismutase （SOD）, ascorbate peroxidase （APX）, glutathione reductase （GR）, catalase （CAT） and guaiacol peroxidase （POD） between waterlogging-tolerant and waterloggingsensitive genotype, we deduced that CAT was the most important H2O2 scavenging enzyme in leaves, while APX seemed to play a key role in roots. POD, APX, GR and CAT activities in conjunction with SOD seem to play an essential protective role in the O2^- and H2O2 scavenging process. Lipid peroxidation was enhanced significantly only in K12 （P 〈 0.001） and there was no difference （P 〉 0.05） in HZ32 up to 6 d after waterlogging stress. These results indicated that oxidative stress may play an important role in waterlogging-stressed maize plants and that the greater protection of HZ32 leaves and roots from waterlogging-induced oxidative damage results, at least in part, through the maintenance of increased antioxidant enzyme activity.展开更多
The Qinshui Basin in the southeastern Shanxi Province is an important area for coalbed methane（CBM） exploration and production in China, and recent exploration has revealed the presence of other unconventional types...The Qinshui Basin in the southeastern Shanxi Province is an important area for coalbed methane（CBM） exploration and production in China, and recent exploration has revealed the presence of other unconventional types of gas such as shale gas and tight sandstone gas. The reservoirs for these unconventional types of gas in this basin are mainly the coals, mudstones, and sandstones of the Carboniferous and Permian; the reservoir thicknesses are controlled by the depositional environments and palaeogeography. This paper presents the results of sedimentological investigations based on data from outcrop and borehole sections, and basin-wide palaeogeographical maps of each formation were reconstructed on the basis of the contours of a variety of lithological parameters. The palaeogeographic units include the depositional environments of the fluvial channel, flood basin（lake）, upper delta plain, lower delta plain, delta front, lagoon, tidal flat, barrier bar, and carbonate platform.The Benxi and Taiyuan Formations are composed mainly of limestones, bauxitic mudstones,siltstones, silty mudstones, sandstones, and economically exploitable coal seams, which were formed in delta, tidal flat, lagoon, and carbonate platform environments. The Shanxi Formation consists of sandstones, siltstones, mudstones, and coals; during the deposition of the formation, the northern part of the Qinshui Basin was occupied mainly by an upper delta plain environment, while the central and southern parts were mainly occupied by a lower delta plain environment and the southeastern part by a delta front environment. Thick coal zones occur in the central and southern parts, where the main depositional environment was a lower delta plain. The thick coal zones of the Taiyuan Formation evidently occur in the sandstone-rich belts, located mainly in the lower delta plain environment in the northern part of the basin and the barrier bar environments in the southeastern part of the basin. In contrast, the thick coal zones of the Shanxi Formation exte展开更多
Background:Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer.This study was performed to develop an automatic and accurate imaging processing technique sys...Background:Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer.This study was performed to develop an automatic and accurate imaging processing technique system,allowing this system to read computed tomography(CT)images correctly and make diagnosis of pancreatic cancer faster.Methods:The establishment of the artificial intelligence(AI)system for pancreatic cancer diagnosis based on sequential contrastenhanced CT images were composed of two processes:training and verification.During training process,our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set.Additionally,we used VGG16,which was pretrained in ImageNet and contained 13 convolutional layers and three fully connected layers,to initialize the feature extraction network.In the verification experiment,we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolution network(Faster R-CNN)model that had completed training.Totally,1699 images from 100 pancreatic cancer patients were included for clinical verification.Results:A total of 338 patients with pancreatic cancer were included in the study.The clinical characteristics(sex,age,tumor location,differentiation grade,and tumor-node-metastasis stage)between the two training and verification groups were insignificant.The mean average precision was 0.7664,indicating a good training ejffect of the Faster R-CNN.Sequential contrastenhanced CT images of 100 pancreatic cancer patients were used for clinical verification.The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632.It took approximately 0.2 s for the Faster R-CNN AI to automatically process one CT image,which is much faster than the time required for diagnosis by an imaging specialist.Conclusions:Faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of panc展开更多
Objective To investigate the association between ambient air pollution and hospital emergency admissions in Beijing. Methods In this study, a semi-parametric generalized additive model （GAM） was used to evaluate the...Objective To investigate the association between ambient air pollution and hospital emergency admissions in Beijing. Methods In this study, a semi-parametric generalized additive model （GAM） was used to evaluate the specific influences of air pollutants （PM10, SO2, and NO2） on hospital emergency admissions with different lag structures from 2009 to 2011, the sex and age specific influences of air pollution and the modifying effect of seasons on air pollution to analyze the possible interaction. Results It was found that a 10μg/m3 increase in concentration of PMlo at lag 03 day, SO2 and NO2 at lag 0 day were associated with an increase of 0.88%, 0.76%, and 1.82% respectively in overall emergency admissions. A 10 lag/m3 increase in concentration of PM10, SO2 and NO2 at lag 5 day were associated with an increase of 1.39%, 1.56%, and 1.18% respectively in cardiovascular disease emergency admissions. For lag 02, a 10 μg/m3 increase in concentration of PM10, SO2 and NO2 were associated with 1.72%, 1.34%, and 2.57% increases respectively in respiratory disease emergency admissions. Conclusion This study further confirmed that short-term exposure to ambient air pollution was associated with increased risk of hospital emergency admissions in Beijing.展开更多
基金supported by the Natural Science Foundation of Hubei Province, China (2008CDB079)the National High Technology Research and Development Program of China (863 Program, 2006AA100103)
文摘To better understand the physiological and biochemical mechanisms of waterlogging tolerance, waterlogging effects on lipid peroxidation and the activity of antioxidative enzymes were investigated in leaves and roots of two maize genotypes, HZ32 （waterlogging-tolerant） and K12 （waterlogging-sensitive）. Potted maize plants were waterlogged at the second leaf stage under glasshouse conditions. Leaves and roots were harvested 1 d before and 2, 4, 6, 8 and 10 d after the start of waterlogging treatment. Through comparing the activities of superoxide dismutase （SOD）, ascorbate peroxidase （APX）, glutathione reductase （GR）, catalase （CAT） and guaiacol peroxidase （POD） between waterlogging-tolerant and waterloggingsensitive genotype, we deduced that CAT was the most important H2O2 scavenging enzyme in leaves, while APX seemed to play a key role in roots. POD, APX, GR and CAT activities in conjunction with SOD seem to play an essential protective role in the O2^- and H2O2 scavenging process. Lipid peroxidation was enhanced significantly only in K12 （P 〈 0.001） and there was no difference （P 〉 0.05） in HZ32 up to 6 d after waterlogging stress. These results indicated that oxidative stress may play an important role in waterlogging-stressed maize plants and that the greater protection of HZ32 leaves and roots from waterlogging-induced oxidative damage results, at least in part, through the maintenance of increased antioxidant enzyme activity.
基金supported by the National Natural Science Foundation of China(No.41572090,No.41472131)the Major National S&T(Science and Technology)Program of China(2016ZX05041004-003)
文摘The Qinshui Basin in the southeastern Shanxi Province is an important area for coalbed methane（CBM） exploration and production in China, and recent exploration has revealed the presence of other unconventional types of gas such as shale gas and tight sandstone gas. The reservoirs for these unconventional types of gas in this basin are mainly the coals, mudstones, and sandstones of the Carboniferous and Permian; the reservoir thicknesses are controlled by the depositional environments and palaeogeography. This paper presents the results of sedimentological investigations based on data from outcrop and borehole sections, and basin-wide palaeogeographical maps of each formation were reconstructed on the basis of the contours of a variety of lithological parameters. The palaeogeographic units include the depositional environments of the fluvial channel, flood basin（lake）, upper delta plain, lower delta plain, delta front, lagoon, tidal flat, barrier bar, and carbonate platform.The Benxi and Taiyuan Formations are composed mainly of limestones, bauxitic mudstones,siltstones, silty mudstones, sandstones, and economically exploitable coal seams, which were formed in delta, tidal flat, lagoon, and carbonate platform environments. The Shanxi Formation consists of sandstones, siltstones, mudstones, and coals; during the deposition of the formation, the northern part of the Qinshui Basin was occupied mainly by an upper delta plain environment, while the central and southern parts were mainly occupied by a lower delta plain environment and the southeastern part by a delta front environment. Thick coal zones occur in the central and southern parts, where the main depositional environment was a lower delta plain. The thick coal zones of the Taiyuan Formation evidently occur in the sandstone-rich belts, located mainly in the lower delta plain environment in the northern part of the basin and the barrier bar environments in the southeastern part of the basin. In contrast, the thick coal zones of the Shanxi Formation exte
基金This work was supported by grants from the National Natural Science Foundation of China(No.81802888)the Key Research and Development Project of Shandong Province(No.2018GSF118206 and No.2018GSF118088).
文摘Background:Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer.This study was performed to develop an automatic and accurate imaging processing technique system,allowing this system to read computed tomography(CT)images correctly and make diagnosis of pancreatic cancer faster.Methods:The establishment of the artificial intelligence(AI)system for pancreatic cancer diagnosis based on sequential contrastenhanced CT images were composed of two processes:training and verification.During training process,our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set.Additionally,we used VGG16,which was pretrained in ImageNet and contained 13 convolutional layers and three fully connected layers,to initialize the feature extraction network.In the verification experiment,we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolution network(Faster R-CNN)model that had completed training.Totally,1699 images from 100 pancreatic cancer patients were included for clinical verification.Results:A total of 338 patients with pancreatic cancer were included in the study.The clinical characteristics(sex,age,tumor location,differentiation grade,and tumor-node-metastasis stage)between the two training and verification groups were insignificant.The mean average precision was 0.7664,indicating a good training ejffect of the Faster R-CNN.Sequential contrastenhanced CT images of 100 pancreatic cancer patients were used for clinical verification.The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632.It took approximately 0.2 s for the Faster R-CNN AI to automatically process one CT image,which is much faster than the time required for diagnosis by an imaging specialist.Conclusions:Faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of panc
基金supported by the Gong-Yi Program of China Meteorological Administration(GYHY201106034)the Fundamental Research Funds for the Central Universities(lzuibky-2013-m03)+2 种基金National Natural Science Foundation of China(41075103)National Natural Science Foundation of China(41075102)National Natural Science Foundation of China(41305105)
文摘Objective To investigate the association between ambient air pollution and hospital emergency admissions in Beijing. Methods In this study, a semi-parametric generalized additive model （GAM） was used to evaluate the specific influences of air pollutants （PM10, SO2, and NO2） on hospital emergency admissions with different lag structures from 2009 to 2011, the sex and age specific influences of air pollution and the modifying effect of seasons on air pollution to analyze the possible interaction. Results It was found that a 10μg/m3 increase in concentration of PMlo at lag 03 day, SO2 and NO2 at lag 0 day were associated with an increase of 0.88%, 0.76%, and 1.82% respectively in overall emergency admissions. A 10 lag/m3 increase in concentration of PM10, SO2 and NO2 at lag 5 day were associated with an increase of 1.39%, 1.56%, and 1.18% respectively in cardiovascular disease emergency admissions. For lag 02, a 10 μg/m3 increase in concentration of PM10, SO2 and NO2 were associated with 1.72%, 1.34%, and 2.57% increases respectively in respiratory disease emergency admissions. Conclusion This study further confirmed that short-term exposure to ambient air pollution was associated with increased risk of hospital emergency admissions in Beijing.
