Many economically important crops and vegetables belonging to the cruciferous family are heavily endangered by clubroot disease caused by Plasmodiophora brassicae infection.Breeding of clubroot resistant cultivars bas...Many economically important crops and vegetables belonging to the cruciferous family are heavily endangered by clubroot disease caused by Plasmodiophora brassicae infection.Breeding of clubroot resistant cultivars based on mapping and cloning of resistant genes is commonly regarded as the most cost-effective and efficient way to fight against this disease.The traditional way of R gene functional validation requires stable transformation that is both time-and labor-consuming.In this study,a rapid and efficient hairy-root transgenic protocol mediated by Agrobacterium rhizogenes was developed.The transformation positive rate was over 80%in Brassica napus showed by GUS reporter gene and this transformation only took 1/6 of the time compared with stable transformation.The system was applicable to different B.napus varieties and other cruciferous crops including Brassica rapa and Brassica oleracea.In particular,two known CR genes,CRA3.7.1 and CRA8.2.4 were used respectively,as example to show that the system works well for CR gene study combined with subsequent P.brassicae infection in B.napus.Most importantly,it works both in over-expression that led to disease resistance,as well as in RNAi which led to disease susceptible phenotype.Therefore,this system can be used in batch-wise identification of CR genes,and also offered the possibility of manipulating key genes within the P.brassicae genome that could improve our knowledge on host-pathogen interaction.展开更多
Objectives:This investigation aimed to elucidate the inhibitory impact of apatinib on the multidrug resistance of liver cancer both in vivo and in vitro.Methods:To establish a Hep3B/5-Fu resistant cell line,5-Fu conce...Objectives:This investigation aimed to elucidate the inhibitory impact of apatinib on the multidrug resistance of liver cancer both in vivo and in vitro.Methods:To establish a Hep3B/5-Fu resistant cell line,5-Fu concentrations were gradually increased in the culture media.Hep3B/5-Fu cells drug resistance and its alleviation by apatinib were confirmed via flow cytometry and Cell Counting Kit 8(CCK8)test.Further,Nuclear factor kappa B(NF-κB)siRNA was transfected into Hep3B/5-Fu cells to assess alterations in the expression of multidrug resistance(MDR)-related genes and proteins.Nude mice were injected with Hep3B/5-Fu cells to establish subcutaneous xenograft tumors and then categorized into 8 treatment groups.The treatments included oxaliplatin,5-Fu,and apatinib.In the tumor tissues,the expression of MDRrelated genes was elucidated via qRT-PCR,immunohistochemistry,and Western blot analyses.Results:The apatinibtreated mice indicated slower tumor growth with smaller size compared to the control group.Both the in vivo and in vitro investigations revealed that the apatinib-treated groups had reduced expression of MDR genes GST-pi,LRP,MDR1,and p-p65.Conclusions:Apatinib effectively suppresses MDR in human hepatic cancer cells by modulating the expression of genes related to MDR,potentially by suppressing the NF-κB signaling pathway.展开更多
The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of o...The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.展开更多
Accurate quantification of the gas hydrate content in the deep sea is useful for assessing the resource potential and understanding the role of gas hydrates in the global carbon cycle.Resistivity logging data combined...Accurate quantification of the gas hydrate content in the deep sea is useful for assessing the resource potential and understanding the role of gas hydrates in the global carbon cycle.Resistivity logging data combined with Archie’s equation are often used to calculate gas hydrate saturation,but the reliability is dependent on the rationality of the empirical parameter cementation factor and saturation index.At present,an increasing number of fine-grained hydrate-rich sediment regions have been discovered worldwide through drilling efforts,and the reservoir types and hydrate distribution are diverse,which differs greatly from that of coarse-grained reservoirs of hydrate-bearing sediment.This results in vertical variations in m and n through stratigraphy.At present,the saturation evaluation effect of these reservoirs cannot be improved.In this work,a theory for the determination of the cementation factor and saturation index was first proposed to obtain reliable and variable values of the empirical parameters.Then,a hydrate saturation evaluation technique with variables m and n was formed based on the well logging data.This technique was used to evaluate complex fine-grained hydrate-bearing reservoirs in several regions worldwide.It was found that the highest n could be 16,and the log calculation results were more consistent with the core hydrate saturation.Additionally,the cause of the excessively high n values was explained from physical principles,and the result was verified with actually well log data.In future evaluations of the amount of hydrate resources in fine-grained sediment reservoirs worldwide,new saturation estimation methods should be taken into account to advance hydrate research.展开更多
For harsh real-world service settings,it is essential to build corrosion-resistant,diverse,and effective microwave absorbers.Herein,we successfully prepared a 3D NiAl-layered double hydroxide/carbon nanofibers(NiAl-LD...For harsh real-world service settings,it is essential to build corrosion-resistant,diverse,and effective microwave absorbers.Herein,we successfully prepared a 3D NiAl-layered double hydroxide/carbon nanofibers(NiAl-LDH/CNFs)composite material as an anticorrosive microwave absorber assisted by an atomic layer deposition(ALD)method.The size,coating thickness,and content of NiAl-LDH can be readily adjusted by changing the ALD cycling numbers.The optimized NiAl-LDH/CNFs demonstrates prominent microwave absorbing properties including the strongest reflection loss of–55.65 dB and the widest effective absorption bandwidth of 4.80 GHz with only 15 wt%loading.The reasons for performance improvement are the cooperative effect of interfacial polarization loss,conduction loss,and three-dimensional porous structure.Moreover,due to the synergistic effects between the excellent impermeability of CNFs and the trapping ability of NiAl-LDH for chloride ions,NiAl-LDH/CNFs exhibits strong corrosion resistances under acidic,neutral,and alkaline conditions.NiAl-LDH/CNFs should be a potential candidate to simultaneously use for microwave absorption and corrosion resistance,and this work provides a certain guiding significance for designing microwave absorbers that satisfy the corrosion resistance.展开更多
The identification of high-quality marine shale gas reservoirs has always been a key task in the exploration and development stage.However,due to the serious nonlinear relationship between the logging curve response a...The identification of high-quality marine shale gas reservoirs has always been a key task in the exploration and development stage.However,due to the serious nonlinear relationship between the logging curve response and high-quality reservoirs,the rapid identification of high-quality reservoirs has always been a problem of low accuracy.This study proposes a combination of the oversampling method and random forest algorithm to improve the identification accuracy of high-quality reservoirs based on logging data.The oversampling method is used to balance the number of samples of different types and the random forest algorithm is used to establish a highprecision and high-quality reservoir identification model.From the perspective of the prediction effect,the reservoir identification method that combines the oversampling method and the random forest algorithm has increased the accuracy of reservoir identification from the 44%seen in other machine learning algorithms to 78%,and the effect is significant.This research can improve the identifiability of high-quality marine shale gas reservoirs,guide the drilling of horizontal wells,and provide tangible help for the precise formulation of marine shale gas development plans.展开更多
Background:Delayed wound healing is one of the major complications of diabetes mellitus and is characterized by prolonged inflammation,delayed re-epithelialization and consistent oxidative stress,although the detailed...Background:Delayed wound healing is one of the major complications of diabetes mellitus and is characterized by prolonged inflammation,delayed re-epithelialization and consistent oxidative stress,although the detailed mechanism remains unknown.In this study,we aimed to investigate the potential role and effect of pterostilbene(PTE)and hematopoietic stem cells(HSCs)on diabetic wound healing.Methods:Diabetic rats were used to measure the epigenetic changes in both HSCs and peripheral blood mononuclear cells(PBMCs).A cutaneous burn injury was induced in the rats and PTE-treated diabetic HSCs were transplanted for evaluation of wound healing.In addition,several biomedical parameters,including gene expression,oxidative stress,mitochondrial function and inflammation in macrophages,were also measured.Results:Our data showed that PTE had a much stronger effect than resveratrol on accelerating diabetic wound healing,likely because PTE can ameliorate diabetes-induced epigenetic changes to estrogen receptorβpromoter in HSCs,while resveratrol cannot.Further investigation showed that bone marrow transplantation of PTE-treated diabetic HSCs restores diabetes-induced suppression of estrogen receptorβand its target genes,including nuclear respiratory factor-1 and superoxide dismutase 2,and protects against diabetes-induced oxidative stress,mitochondrial dysfunction and elevated pro-inflammatory cytokines in both PBMCs and macrophages,subsequently accelerating cutaneous wound healing.Conclusions:HSC may play an important role in wound healing through transferring epigenetic modifications to subsequent PBMCs and macrophages by differentiation,while PTE accelerates diabetic wound healing by modulating diabetes-induced epigenetic changes in HSCs.Thus,PTE may be a novel therapeutic strategy for diabetic wound healing.展开更多
The original version of this article(Liu et al.,2020)unfortunately contained some mistakes.1.Figs.7c and 7d in p.229 were incorrect.The upper left and bottom left pictures in Fig.7c were accidentally duplicated with t...The original version of this article(Liu et al.,2020)unfortunately contained some mistakes.1.Figs.7c and 7d in p.229 were incorrect.The upper left and bottom left pictures in Fig.7c were accidentally duplicated with the pictures at the same position of Fig.la.The upper right and bottom right pictures were mistakenly placed in Fig.展开更多
基金supported by grants from the Wuhan Science and Technology Major Project on Key techniques of biological breeding and Breeding of new varieties(Grant No.2022021302024851)the special project for sustainable development agenda of innovation demonstration zone(Grant No.202204AC100001-A04)the National Key R&D Program of China(Grant No.2022YFD1200400)。
文摘Many economically important crops and vegetables belonging to the cruciferous family are heavily endangered by clubroot disease caused by Plasmodiophora brassicae infection.Breeding of clubroot resistant cultivars based on mapping and cloning of resistant genes is commonly regarded as the most cost-effective and efficient way to fight against this disease.The traditional way of R gene functional validation requires stable transformation that is both time-and labor-consuming.In this study,a rapid and efficient hairy-root transgenic protocol mediated by Agrobacterium rhizogenes was developed.The transformation positive rate was over 80%in Brassica napus showed by GUS reporter gene and this transformation only took 1/6 of the time compared with stable transformation.The system was applicable to different B.napus varieties and other cruciferous crops including Brassica rapa and Brassica oleracea.In particular,two known CR genes,CRA3.7.1 and CRA8.2.4 were used respectively,as example to show that the system works well for CR gene study combined with subsequent P.brassicae infection in B.napus.Most importantly,it works both in over-expression that led to disease resistance,as well as in RNAi which led to disease susceptible phenotype.Therefore,this system can be used in batch-wise identification of CR genes,and also offered the possibility of manipulating key genes within the P.brassicae genome that could improve our knowledge on host-pathogen interaction.
基金supported by grants from the National Natural Science Foundation of China(No.82272986 to SY)the Natural Science Foundation of Guangdong Province,China(No.2023A1515010230 to SY)+1 种基金the Science and Technology Foundation of Shenzhen(No.JCYJ20220531094805012 to SY)the Scientific Research Project of Shenzhen Pingshan District Health System(202060 to SY).
文摘Objectives:This investigation aimed to elucidate the inhibitory impact of apatinib on the multidrug resistance of liver cancer both in vivo and in vitro.Methods:To establish a Hep3B/5-Fu resistant cell line,5-Fu concentrations were gradually increased in the culture media.Hep3B/5-Fu cells drug resistance and its alleviation by apatinib were confirmed via flow cytometry and Cell Counting Kit 8(CCK8)test.Further,Nuclear factor kappa B(NF-κB)siRNA was transfected into Hep3B/5-Fu cells to assess alterations in the expression of multidrug resistance(MDR)-related genes and proteins.Nude mice were injected with Hep3B/5-Fu cells to establish subcutaneous xenograft tumors and then categorized into 8 treatment groups.The treatments included oxaliplatin,5-Fu,and apatinib.In the tumor tissues,the expression of MDRrelated genes was elucidated via qRT-PCR,immunohistochemistry,and Western blot analyses.Results:The apatinibtreated mice indicated slower tumor growth with smaller size compared to the control group.Both the in vivo and in vitro investigations revealed that the apatinib-treated groups had reduced expression of MDR genes GST-pi,LRP,MDR1,and p-p65.Conclusions:Apatinib effectively suppresses MDR in human hepatic cancer cells by modulating the expression of genes related to MDR,potentially by suppressing the NF-κB signaling pathway.
基金This project was funded by the Open Fund of the Key Laboratory of Exploration Technologies for Oil and Gas Resources,the Ministry of Education(No.K2021-03)National Natural Science Foundation of China(No.42106213)+2 种基金the Hainan Provincial Natural Science Foundation of China(No.421QN281)the China Postdoctoral Science Foundation(Nos.2021M690161 and 2021T140691)the Postdoctorate Funded Project in Hainan Province.
文摘The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.
基金This project was funded by the National Natural Science Foundation of China(No.42106213)the China Postdoctoral Science Foundation(Nos.2021M690161 and 2021T140691)+1 种基金the National Key Research and Development Program of China(No.2021YFC3100601)the Postdoctorate Funded Project in Hainan Province.
文摘Accurate quantification of the gas hydrate content in the deep sea is useful for assessing the resource potential and understanding the role of gas hydrates in the global carbon cycle.Resistivity logging data combined with Archie’s equation are often used to calculate gas hydrate saturation,but the reliability is dependent on the rationality of the empirical parameter cementation factor and saturation index.At present,an increasing number of fine-grained hydrate-rich sediment regions have been discovered worldwide through drilling efforts,and the reservoir types and hydrate distribution are diverse,which differs greatly from that of coarse-grained reservoirs of hydrate-bearing sediment.This results in vertical variations in m and n through stratigraphy.At present,the saturation evaluation effect of these reservoirs cannot be improved.In this work,a theory for the determination of the cementation factor and saturation index was first proposed to obtain reliable and variable values of the empirical parameters.Then,a hydrate saturation evaluation technique with variables m and n was formed based on the well logging data.This technique was used to evaluate complex fine-grained hydrate-bearing reservoirs in several regions worldwide.It was found that the highest n could be 16,and the log calculation results were more consistent with the core hydrate saturation.Additionally,the cause of the excessively high n values was explained from physical principles,and the result was verified with actually well log data.In future evaluations of the amount of hydrate resources in fine-grained sediment reservoirs worldwide,new saturation estimation methods should be taken into account to advance hydrate research.
基金supported by the National Natural Science Foundation of China(Grant Nos.22068010,22278101,and 22168016)the Finance Science and Technology Project of Hainan Province(Grant Nos.ZDYF2020009)the Natural Science Foundation of Hainan Province(Grant Nos.2019RC142 and 519QN176).
文摘For harsh real-world service settings,it is essential to build corrosion-resistant,diverse,and effective microwave absorbers.Herein,we successfully prepared a 3D NiAl-layered double hydroxide/carbon nanofibers(NiAl-LDH/CNFs)composite material as an anticorrosive microwave absorber assisted by an atomic layer deposition(ALD)method.The size,coating thickness,and content of NiAl-LDH can be readily adjusted by changing the ALD cycling numbers.The optimized NiAl-LDH/CNFs demonstrates prominent microwave absorbing properties including the strongest reflection loss of–55.65 dB and the widest effective absorption bandwidth of 4.80 GHz with only 15 wt%loading.The reasons for performance improvement are the cooperative effect of interfacial polarization loss,conduction loss,and three-dimensional porous structure.Moreover,due to the synergistic effects between the excellent impermeability of CNFs and the trapping ability of NiAl-LDH for chloride ions,NiAl-LDH/CNFs exhibits strong corrosion resistances under acidic,neutral,and alkaline conditions.NiAl-LDH/CNFs should be a potential candidate to simultaneously use for microwave absorption and corrosion resistance,and this work provides a certain guiding significance for designing microwave absorbers that satisfy the corrosion resistance.
基金This project was funded by the Laboratory for Marine Geology,Qingdao National Laboratory for Marine Science and Technology,(MGQNLM-KF202004)China Postdoctoral Science Foundation(2021M690161,2021T140691)+2 种基金Postdoctoral Funded Project in Hainan Province(General Program)Chinese Academy of Sciences-Special Research Assistant Projectthe Open Fund of Key Laboratory of Exploration Technologies for Oil and Gas Resources(Yangtze University),Ministry of Education(No.K2021–03,K2021-08)。
文摘The identification of high-quality marine shale gas reservoirs has always been a key task in the exploration and development stage.However,due to the serious nonlinear relationship between the logging curve response and high-quality reservoirs,the rapid identification of high-quality reservoirs has always been a problem of low accuracy.This study proposes a combination of the oversampling method and random forest algorithm to improve the identification accuracy of high-quality reservoirs based on logging data.The oversampling method is used to balance the number of samples of different types and the random forest algorithm is used to establish a highprecision and high-quality reservoir identification model.From the perspective of the prediction effect,the reservoir identification method that combines the oversampling method and the random forest algorithm has increased the accuracy of reservoir identification from the 44%seen in other machine learning algorithms to 78%,and the effect is significant.This research can improve the identifiability of high-quality marine shale gas reservoirs,guide the drilling of horizontal wells,and provide tangible help for the precise formulation of marine shale gas development plans.
基金supported by the National Natural Science Foundation of China Project(81772097)the National Key Disease Preventive Project for Wound Healing(2018-ZX-01S-001).
文摘Background:Delayed wound healing is one of the major complications of diabetes mellitus and is characterized by prolonged inflammation,delayed re-epithelialization and consistent oxidative stress,although the detailed mechanism remains unknown.In this study,we aimed to investigate the potential role and effect of pterostilbene(PTE)and hematopoietic stem cells(HSCs)on diabetic wound healing.Methods:Diabetic rats were used to measure the epigenetic changes in both HSCs and peripheral blood mononuclear cells(PBMCs).A cutaneous burn injury was induced in the rats and PTE-treated diabetic HSCs were transplanted for evaluation of wound healing.In addition,several biomedical parameters,including gene expression,oxidative stress,mitochondrial function and inflammation in macrophages,were also measured.Results:Our data showed that PTE had a much stronger effect than resveratrol on accelerating diabetic wound healing,likely because PTE can ameliorate diabetes-induced epigenetic changes to estrogen receptorβpromoter in HSCs,while resveratrol cannot.Further investigation showed that bone marrow transplantation of PTE-treated diabetic HSCs restores diabetes-induced suppression of estrogen receptorβand its target genes,including nuclear respiratory factor-1 and superoxide dismutase 2,and protects against diabetes-induced oxidative stress,mitochondrial dysfunction and elevated pro-inflammatory cytokines in both PBMCs and macrophages,subsequently accelerating cutaneous wound healing.Conclusions:HSC may play an important role in wound healing through transferring epigenetic modifications to subsequent PBMCs and macrophages by differentiation,while PTE accelerates diabetic wound healing by modulating diabetes-induced epigenetic changes in HSCs.Thus,PTE may be a novel therapeutic strategy for diabetic wound healing.
文摘The original version of this article(Liu et al.,2020)unfortunately contained some mistakes.1.Figs.7c and 7d in p.229 were incorrect.The upper left and bottom left pictures in Fig.7c were accidentally duplicated with the pictures at the same position of Fig.la.The upper right and bottom right pictures were mistakenly placed in Fig.
