This research classified vegetation types and evaluated net primary productivity （NPP） of southern China＇s grasslands based on the improved comprehensive and sequential classification system （CSCS）, and proposed ...This research classified vegetation types and evaluated net primary productivity （NPP） of southern China＇s grasslands based on the improved comprehensive and sequential classification system （CSCS）, and proposed 5 thermal grades and 6 humidity grades. Four classes of grasslands vegetation were recognized by improved CSCS, namely, tundra grassland class, typical grassland class, mixed grassland class and alpine grassland class. At the type level, 14 types of vegetations （9 grasslands and 5 forests） were classified. The NPP had a trend to decrease from east to west and south to north, and the annual mean NPP was estimated to be 656.3 g C m-2 yr-1. The NPP value of alpine grassland class was relatively high, generally more than 1200 g C m2 yr-1. The NPP value of mixed grassland class was in a range from 1 000 to 1200 g C m-2 yr-1. Tundra grassland class was located in southeastern Tibet with high elevation, and its NPP value was the lowest （〈600 g C m＇2yrl）. The typical grassland class distributed in most of the area, and its NPP value was generally from 600 to 1000 g C m-2 yr-1. The total NPP value in the study area was 68.46 Tg C. The NPP value of typical grassland class was the highest （48.44 Tg C）, and mixed grassland class was the second （16.54 Tg C）, followed by alpine grassland class （3.22 Tg C）, with tundra grassland class being the lowest （0.25 Tg C）. For all the grasslands types, the total NPP of forest meadow was the highest （34.81 Tg C）, followed by sparse forest brush （16.54 Tg C）, and montane meadow was the lowest （0.01 Tg C）.展开更多
Potential natural vegetation(PNV)is a valuable reference for ecosystem renovation and has garnered increasing attention worldwide.However,there is limited knowledge on the spatio-temporal distributions,transitional pr...Potential natural vegetation(PNV)is a valuable reference for ecosystem renovation and has garnered increasing attention worldwide.However,there is limited knowledge on the spatio-temporal distributions,transitional processes,and underlying mechanisms of global natural vegetation,particularly in the case of ongoing climate warming.In this study,we visualize the spatio-temporal pattern and inter-transition procedure of global PNV,analyse the shifting distances and directions of global PNV under the influence of climatic disturbance,and explore the mechanisms of global PNV in response to temperature and precipitation fluctuations.To achieve this,we utilize meteorological data,mainly temperature and precipitation,from six phases:the Last Inter-Glacial(LIG),the Last Glacial Maximum(LGM),the Mid Holocene(MH),the Present Day(PD),2030(20212040)and 2090(2081–2100),and employ a widely-accepted comprehensive and sequential classification sy–stem(CSCS)for global PNV classification.We find that the spatial patterns of five PNV groups(forest,shrubland,savanna,grassland and tundra)generally align with their respective ecotopes,although their distributions have shifted due to fluctuating temperature and precipitation.Notably,we observe an unexpected transition between tundra and savanna despite their geographical distance.The shifts in distance and direction of five PNV groups are mainly driven by temperature and precipitation,although there is heterogeneity among these shifts for each group.Indeed,the heterogeneity observed among different global PNV groups suggests that they may possess varying capacities to adjust to and withstand the impacts of changing climate.The spatio-temporal distributions,mutual transitions and shift tendencies of global PNV and its underlying mechanism in face of changing climate,as revealed in this study,can significantly contribute to the development of strategies for mitigating warming and promoting re-vegetation in degraded regions worldwide.展开更多
The pulse-width-modulated(PWM)current-source converters(CSCs)fed electric machine systems can be considered as a type of high reliability energy conversion systems,since they work with the long-life DC-link inductor a...The pulse-width-modulated(PWM)current-source converters(CSCs)fed electric machine systems can be considered as a type of high reliability energy conversion systems,since they work with the long-life DC-link inductor and offer high fault-tolerant capability for short-circuit faults.Besides,they provide motor friendly waveforms and four-quadrant operation ability.Therefore,they are suitable for high-power applications of fans,pumps,compressors and wind power generation.The purpose of this paper is to comprehensively review recent developments of key technologies on modulation and control of high-power(HP)PWM-CSC fed electric machines systems,including reduction of low-order current harmonics,suppression of inductor–capacitor(LC)resonance,mitigation of common-mode voltage(CMV)and control of modular PWM-CSC fed systems.In particular,recent work on the overlapping effects during commutation,LC resonance suppression under fault-tolerant operation and collaboration of modular PMW-CSCs are described.Both theoretical analysis and some results in simulations and experiments are presented.Finally,a brief discussion regarding the future trend of the HP CSC fed electric machines systems is presented.展开更多
Objective:Pancreatic ductal adenocarcinoma(PDAC)is an aggressive malignancy.CD8^(+)T cells,cancer stem cells(CSCs),and tumor budding(TB)have been significantly correlated with the outcome of patients with PDAC,but the...Objective:Pancreatic ductal adenocarcinoma(PDAC)is an aggressive malignancy.CD8^(+)T cells,cancer stem cells(CSCs),and tumor budding(TB)have been significantly correlated with the outcome of patients with PDAC,but the correlations have been independently reported.In addition,no integrated immune-CSC-TB profile for predicting survival in patients with PDAC has been established.Methods:Multiplexed immunofluorescence and artificial intelligence(AI)-based comprehensive analyses were used for quantification and spatial distribution analysis of CD8^(+)T cells,CD133^(+)CSCs,and TB.In vivo humanized patient-derived xenograft(PDX)models were established.Nomogram analysis,calibration curve,time-dependent receiver operating characteristic curve,and decision curve analyses were performed using R software.Results:The established‘anti-/pro-tumor’models showed that the CD8^(+)T cell/TB,CD8^(+)T cell/CD133^(+)CSC,TB-adjacent CD8^(+)T cell,and CD133^(+)CSC-adjacent CD8^(+)T cell indices were positively associated with survival of patients with PDAC.These findings were validated using PDX-transplanted humanized mouse models.An integrated nomogram-based immune-CSC-TB profile that included the CD8^(+)T cell/TB and CD8^(+)T cell/CD133^(+)CSC indices was established and shown to be superior to the tumor-nodemetastasis stage model in predicting survival of patients with PDAC.Conclusions:‘Anti-/pro-tumor’models and the spatial relationship among CD8^(+)T cells,CSCs,and TB within the tumor microenvironment were investigated.Novel strategies to predict the prognosis of patients with PDAC were established using AI-based comprehensive analysis and machine learning workflow.The nomogram-based immune-CSC-TB profile can provide accurate prognosis prediction for patients with PDAC.展开更多
基金the National Basic Research Program of China(2010CB950702)the National High-Technology Reaearch and Development Program of China(2007AA10Z231)the Asia-Pacific Network for Global Change Research Project(ARCP201106CMY-Li)
文摘This research classified vegetation types and evaluated net primary productivity （NPP） of southern China＇s grasslands based on the improved comprehensive and sequential classification system （CSCS）, and proposed 5 thermal grades and 6 humidity grades. Four classes of grasslands vegetation were recognized by improved CSCS, namely, tundra grassland class, typical grassland class, mixed grassland class and alpine grassland class. At the type level, 14 types of vegetations （9 grasslands and 5 forests） were classified. The NPP had a trend to decrease from east to west and south to north, and the annual mean NPP was estimated to be 656.3 g C m-2 yr-1. The NPP value of alpine grassland class was relatively high, generally more than 1200 g C m2 yr-1. The NPP value of mixed grassland class was in a range from 1 000 to 1200 g C m-2 yr-1. Tundra grassland class was located in southeastern Tibet with high elevation, and its NPP value was the lowest （〈600 g C m＇2yrl）. The typical grassland class distributed in most of the area, and its NPP value was generally from 600 to 1000 g C m-2 yr-1. The total NPP value in the study area was 68.46 Tg C. The NPP value of typical grassland class was the highest （48.44 Tg C）, and mixed grassland class was the second （16.54 Tg C）, followed by alpine grassland class （3.22 Tg C）, with tundra grassland class being the lowest （0.25 Tg C）. For all the grasslands types, the total NPP of forest meadow was the highest （34.81 Tg C）, followed by sparse forest brush （16.54 Tg C）, and montane meadow was the lowest （0.01 Tg C）.
基金funded by the National Natural Science Foundation of China(grants No.30960264,31160475 and 42071258)Open Research Fund of TPESER(grant No.TPESER202208)+2 种基金Special Fund for Basic Scientific Research of Central Colleges,Chang’an University,China(grant No.300102353501)Natural Science Foundation of Gansu Province,China(grant No.22JR5RA857)Higher Education Novel Foundation of Gansu Province,China(grant No.2021B-130)。
文摘Potential natural vegetation(PNV)is a valuable reference for ecosystem renovation and has garnered increasing attention worldwide.However,there is limited knowledge on the spatio-temporal distributions,transitional processes,and underlying mechanisms of global natural vegetation,particularly in the case of ongoing climate warming.In this study,we visualize the spatio-temporal pattern and inter-transition procedure of global PNV,analyse the shifting distances and directions of global PNV under the influence of climatic disturbance,and explore the mechanisms of global PNV in response to temperature and precipitation fluctuations.To achieve this,we utilize meteorological data,mainly temperature and precipitation,from six phases:the Last Inter-Glacial(LIG),the Last Glacial Maximum(LGM),the Mid Holocene(MH),the Present Day(PD),2030(20212040)and 2090(2081–2100),and employ a widely-accepted comprehensive and sequential classification sy–stem(CSCS)for global PNV classification.We find that the spatial patterns of five PNV groups(forest,shrubland,savanna,grassland and tundra)generally align with their respective ecotopes,although their distributions have shifted due to fluctuating temperature and precipitation.Notably,we observe an unexpected transition between tundra and savanna despite their geographical distance.The shifts in distance and direction of five PNV groups are mainly driven by temperature and precipitation,although there is heterogeneity among these shifts for each group.Indeed,the heterogeneity observed among different global PNV groups suggests that they may possess varying capacities to adjust to and withstand the impacts of changing climate.The spatio-temporal distributions,mutual transitions and shift tendencies of global PNV and its underlying mechanism in face of changing climate,as revealed in this study,can significantly contribute to the development of strategies for mitigating warming and promoting re-vegetation in degraded regions worldwide.
基金supported in part by the Jiangsu Natural Science Foundation of China under Grant BK20180013in part by the Shenzhen Science and Technology Innovation Committee(STIC)under Grant JCYJ20180306174439784.
文摘The pulse-width-modulated(PWM)current-source converters(CSCs)fed electric machine systems can be considered as a type of high reliability energy conversion systems,since they work with the long-life DC-link inductor and offer high fault-tolerant capability for short-circuit faults.Besides,they provide motor friendly waveforms and four-quadrant operation ability.Therefore,they are suitable for high-power applications of fans,pumps,compressors and wind power generation.The purpose of this paper is to comprehensively review recent developments of key technologies on modulation and control of high-power(HP)PWM-CSC fed electric machines systems,including reduction of low-order current harmonics,suppression of inductor–capacitor(LC)resonance,mitigation of common-mode voltage(CMV)and control of modular PWM-CSC fed systems.In particular,recent work on the overlapping effects during commutation,LC resonance suppression under fault-tolerant operation and collaboration of modular PMW-CSCs are described.Both theoretical analysis and some results in simulations and experiments are presented.Finally,a brief discussion regarding the future trend of the HP CSC fed electric machines systems is presented.
基金supported by The Science&Technology Development Fund of Tianjin Education Commission for Higher Education(Grant No.2017KJ198)。
文摘Objective:Pancreatic ductal adenocarcinoma(PDAC)is an aggressive malignancy.CD8^(+)T cells,cancer stem cells(CSCs),and tumor budding(TB)have been significantly correlated with the outcome of patients with PDAC,but the correlations have been independently reported.In addition,no integrated immune-CSC-TB profile for predicting survival in patients with PDAC has been established.Methods:Multiplexed immunofluorescence and artificial intelligence(AI)-based comprehensive analyses were used for quantification and spatial distribution analysis of CD8^(+)T cells,CD133^(+)CSCs,and TB.In vivo humanized patient-derived xenograft(PDX)models were established.Nomogram analysis,calibration curve,time-dependent receiver operating characteristic curve,and decision curve analyses were performed using R software.Results:The established‘anti-/pro-tumor’models showed that the CD8^(+)T cell/TB,CD8^(+)T cell/CD133^(+)CSC,TB-adjacent CD8^(+)T cell,and CD133^(+)CSC-adjacent CD8^(+)T cell indices were positively associated with survival of patients with PDAC.These findings were validated using PDX-transplanted humanized mouse models.An integrated nomogram-based immune-CSC-TB profile that included the CD8^(+)T cell/TB and CD8^(+)T cell/CD133^(+)CSC indices was established and shown to be superior to the tumor-nodemetastasis stage model in predicting survival of patients with PDAC.Conclusions:‘Anti-/pro-tumor’models and the spatial relationship among CD8^(+)T cells,CSCs,and TB within the tumor microenvironment were investigated.Novel strategies to predict the prognosis of patients with PDAC were established using AI-based comprehensive analysis and machine learning workflow.The nomogram-based immune-CSC-TB profile can provide accurate prognosis prediction for patients with PDAC.
