Flowering time is an important agronomic trait that contributes to fitness in plants.However,the genetic basis of flowering time has not been extensively studied in pepper.To understand the genetics underlying floweri...Flowering time is an important agronomic trait that contributes to fitness in plants.However,the genetic basis of flowering time has not been extensively studied in pepper.To understand the genetics underlying flowering time,we constructed an F 2 population by crossing a spontaneous early flowering mutant and a late-flowering pepper line.Using bulked segregant RNA-seq,a major locus controlling flowering time in this population was mapped to the end of chromosome 2.An APETALA2(AP2)homolog(CaFFN)cosegregated with flowering time in 297 individuals of the F 2 population.A comparison between the parents revealed a naturally occurring rare SNP(SNP2T>C)that resulted in the loss of a start codon in CaFFN in the early flowering mutant.Transgenic Nicotiana benthamiana plants with high CaFFN expression exhibited a delay in flowering time and floral patterning defects.On the other hand,pepper plants with CaFFN silencing flowered early.Therefore,the CaFFN gene acts as a flowering repressor in pepper.CaFFN may function as a transcriptional activator to activate the expression of CaAGL15 and miR156e and as a transcriptional repressor to repress the expression of CaAG,CaAP1,CaSEP3,CaSOC1,and miR172b based on a qRT-PCR assay.Direct activation of CaAGL15 by CaFFN was detected using yeast one-hybrid and dual-luciferase reporter assays,consistent with the hypothesis that CaFFN regulates flowering time.Moreover,the CaFFN gene association analysis revealed a significant association with flowering time in a natural pepper population,indicating that the CaFFN gene has a broad effect on flowering time in pepper.Finally,the phylogeny,evolutionary expansion and expression patterns of CaFFN/AP2 homologs were analyzed to provide valuable insight into CaFFN.This study increases our understanding of the involvement of CaFFN in controlling flowering time in pepper,thus making CaFFN a target gene for breeding early maturing pepper.展开更多
As a high efficiency hydrogen-to-power device,proton exchange membrane fuel cell(PEMFC)attracts much attention,especially for the automotive applications.Real-time prediction of output voltage and area specific resist...As a high efficiency hydrogen-to-power device,proton exchange membrane fuel cell(PEMFC)attracts much attention,especially for the automotive applications.Real-time prediction of output voltage and area specific resistance(ASR)via the on-board model is critical to monitor the health state of the automotive PEMFC stack.In this study,we use a transient PEMFC system model for dynamic process simulation of PEMFC to generate the dataset,and a long short-term memory(LSTM)deep learning model is developed to predict the dynamic per-formance of PEMFC.The results show that the developed LSTM deep learning model has much better perfor-mance than other models.A sensitivity analysis on the input features is performed,and three insensitive features are removed,that could slightly improve the prediction accuracy and significantly reduce the data volume.The neural structure,sequence duration,and sampling frequency are optimized.We find that the optimal sequence data duration for predicting ASR is 5 s or 20 s,and that for predicting output voltage is 40 s.The sampling frequency can be reduced from 10 Hz to 0.5 Hz and 0.25 Hz,which slightly affects the prediction accuracy,but obviously reduces the data volume and computation amount.展开更多
Data-driven modelling methods are being developed in the quest to achieve more accurate performance prediction of protons exchange membrane fuel cell (PEMFC) systems in response to their complicated physicochemical ph...Data-driven modelling methods are being developed in the quest to achieve more accurate performance prediction of protons exchange membrane fuel cell (PEMFC) systems in response to their complicated physicochemical phenomena. However, there is little research in this field detailing the pre-processing and selection of balance of plants (BOP) features for the input layer of system performance prediction at different current densities. Furthermore, most of the previous research applies neural networks based on simulation data rather than real-time bench or vehicle operation datasets which leads to low robustness and unreliable practical results. This paper details the application of a novel algorithm denoted XGBoost-Boruta, which utilises the combination of an ensemble learning approach and a wrapping approach, to improve the robustness of feature selection and to increase the accuracy and robustness of PEMFC system performance prediction. By introduction of the Z score and shadow features to eliminate the randomness of conventional ensemble learning methods, seven key controllable BOP variables of the hydrogen anode, air cathode and cooling subsystems are selected as the original input variables to determine their dependency on the stack voltage. Two case studies are presented for verification and validation of the proposed algorithm based on the real-time dataset of bench experimental data and data obtained from heavy truck operation at current densities ranging from 100 to 1500 mA/cm2. The feature selection strategy, based on the proposed XGBoost-Boruta algorithm, largely decreases the RMSE by 23.8% and 14.1% and the R^(2) increases by 0.06 and 0.04 of both the bench experimental and the heavy truck validation datasets respectively.展开更多
Detecting changes in vegetation,distinguishing the persistence of changes,and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics.Using the Global Invent...Detecting changes in vegetation,distinguishing the persistence of changes,and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics.Using the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index(NDVI)version NDVI_(3g) dataset in the Tibetan Plateau,the trends in the seasonal components of NDVI and their linkage with climatic factors were analyzed over 14 asymptotic periods of 18–31 years since 1982.Dynamic trends in vegetation experienced an obvious increase at regional scale,but the increases of vegetation activity mostly tended to stall or slow down as the studied time period was extended.At pixel scale,areas with significant browning significantly expanded over 14 periods for all seasons,but for significant greening significantly increased only in autumn.The changes of vegetation activity in spring were the most drastic among three seasons.Increased increments of NDVI in summer,spring,and autumn took turns being the main reason for the enhanced vegetation activity in the growing season in the nested 14 periods.Vegetation activity was mainly regulated by a thermal factor,and the dominant climatic drivers of vegetation growth varied across different seasons and regions.We speculate that the increase of NDVI will continue but the increments will decline in all seasons except autumn.展开更多
基金This research was supported by the National Natural Science Foundation of China(31660574)China Postdoctoral Science Foundation(2020M671969)+2 种基金Agricultural Collaborative Innovation Project of Jiangxi Province of China(JXXTCXQN202001)China Agriculture Research System(CARS-24-G-08)Key Research and Development Program of Jiangxi Province of China(20202BBF62002).
文摘Flowering time is an important agronomic trait that contributes to fitness in plants.However,the genetic basis of flowering time has not been extensively studied in pepper.To understand the genetics underlying flowering time,we constructed an F 2 population by crossing a spontaneous early flowering mutant and a late-flowering pepper line.Using bulked segregant RNA-seq,a major locus controlling flowering time in this population was mapped to the end of chromosome 2.An APETALA2(AP2)homolog(CaFFN)cosegregated with flowering time in 297 individuals of the F 2 population.A comparison between the parents revealed a naturally occurring rare SNP(SNP2T>C)that resulted in the loss of a start codon in CaFFN in the early flowering mutant.Transgenic Nicotiana benthamiana plants with high CaFFN expression exhibited a delay in flowering time and floral patterning defects.On the other hand,pepper plants with CaFFN silencing flowered early.Therefore,the CaFFN gene acts as a flowering repressor in pepper.CaFFN may function as a transcriptional activator to activate the expression of CaAGL15 and miR156e and as a transcriptional repressor to repress the expression of CaAG,CaAP1,CaSEP3,CaSOC1,and miR172b based on a qRT-PCR assay.Direct activation of CaAGL15 by CaFFN was detected using yeast one-hybrid and dual-luciferase reporter assays,consistent with the hypothesis that CaFFN regulates flowering time.Moreover,the CaFFN gene association analysis revealed a significant association with flowering time in a natural pepper population,indicating that the CaFFN gene has a broad effect on flowering time in pepper.Finally,the phylogeny,evolutionary expansion and expression patterns of CaFFN/AP2 homologs were analyzed to provide valuable insight into CaFFN.This study increases our understanding of the involvement of CaFFN in controlling flowering time in pepper,thus making CaFFN a target gene for breeding early maturing pepper.
基金This research is supported by the National Natural Science Founda-tion of China(No.52176196)the National Key Research and Devel-opment Program of China(No.2022YFE0103100)+1 种基金the China Postdoctoral Science Foundation(No.2021TQ0235)the Hong Kong Scholars Program(No.XJ2021033).
文摘As a high efficiency hydrogen-to-power device,proton exchange membrane fuel cell(PEMFC)attracts much attention,especially for the automotive applications.Real-time prediction of output voltage and area specific resistance(ASR)via the on-board model is critical to monitor the health state of the automotive PEMFC stack.In this study,we use a transient PEMFC system model for dynamic process simulation of PEMFC to generate the dataset,and a long short-term memory(LSTM)deep learning model is developed to predict the dynamic per-formance of PEMFC.The results show that the developed LSTM deep learning model has much better perfor-mance than other models.A sensitivity analysis on the input features is performed,and three insensitive features are removed,that could slightly improve the prediction accuracy and significantly reduce the data volume.The neural structure,sequence duration,and sampling frequency are optimized.We find that the optimal sequence data duration for predicting ASR is 5 s or 20 s,and that for predicting output voltage is 40 s.The sampling frequency can be reduced from 10 Hz to 0.5 Hz and 0.25 Hz,which slightly affects the prediction accuracy,but obviously reduces the data volume and computation amount.
文摘Data-driven modelling methods are being developed in the quest to achieve more accurate performance prediction of protons exchange membrane fuel cell (PEMFC) systems in response to their complicated physicochemical phenomena. However, there is little research in this field detailing the pre-processing and selection of balance of plants (BOP) features for the input layer of system performance prediction at different current densities. Furthermore, most of the previous research applies neural networks based on simulation data rather than real-time bench or vehicle operation datasets which leads to low robustness and unreliable practical results. This paper details the application of a novel algorithm denoted XGBoost-Boruta, which utilises the combination of an ensemble learning approach and a wrapping approach, to improve the robustness of feature selection and to increase the accuracy and robustness of PEMFC system performance prediction. By introduction of the Z score and shadow features to eliminate the randomness of conventional ensemble learning methods, seven key controllable BOP variables of the hydrogen anode, air cathode and cooling subsystems are selected as the original input variables to determine their dependency on the stack voltage. Two case studies are presented for verification and validation of the proposed algorithm based on the real-time dataset of bench experimental data and data obtained from heavy truck operation at current densities ranging from 100 to 1500 mA/cm2. The feature selection strategy, based on the proposed XGBoost-Boruta algorithm, largely decreases the RMSE by 23.8% and 14.1% and the R^(2) increases by 0.06 and 0.04 of both the bench experimental and the heavy truck validation datasets respectively.
基金supported by the National Key Research and Development Plan of China[grant number 2016YFC0500401-5]the National Natural Science Foundation of China[grant number 41001055].
文摘Detecting changes in vegetation,distinguishing the persistence of changes,and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics.Using the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index(NDVI)version NDVI_(3g) dataset in the Tibetan Plateau,the trends in the seasonal components of NDVI and their linkage with climatic factors were analyzed over 14 asymptotic periods of 18–31 years since 1982.Dynamic trends in vegetation experienced an obvious increase at regional scale,but the increases of vegetation activity mostly tended to stall or slow down as the studied time period was extended.At pixel scale,areas with significant browning significantly expanded over 14 periods for all seasons,but for significant greening significantly increased only in autumn.The changes of vegetation activity in spring were the most drastic among three seasons.Increased increments of NDVI in summer,spring,and autumn took turns being the main reason for the enhanced vegetation activity in the growing season in the nested 14 periods.Vegetation activity was mainly regulated by a thermal factor,and the dominant climatic drivers of vegetation growth varied across different seasons and regions.We speculate that the increase of NDVI will continue but the increments will decline in all seasons except autumn.
