The leaf nitrogen(N)to phosphorus(P)ratio(N:P)is a critical indicator of nutrient dynamics and ecosystem function.Investigating temporal variations in leaf N:P can provide valuable insights into how plants adapt to en...The leaf nitrogen(N)to phosphorus(P)ratio(N:P)is a critical indicator of nutrient dynamics and ecosystem function.Investigating temporal variations in leaf N:P can provide valuable insights into how plants adapt to environmental changes and nutrient availability.However,limited research has been conducted on long-term temporal leaf N:P variation over a range of temperature zones.Using long-term monitoring data from the Chinese Ecosystem Research Network(CERN),we investigated temporal changes in leaf N and P stoichiometry for 50 dominant tree species from 10 typical forest sites across temperate and subtropical regions,and identified the underlying mechanisms driving these changes.For both regions combined,leaf P concentration of the 50 dominant tree species decreased(20.6%),whereas leaf N:P increased(52.0%)from 2005 to 2020.Leaf P decreased and leaf N:P increased in 67% and 69% of the tree species,respectively.The leaf N:P increase was primarily driven by the tree species in eastern subtropical forests,where global change factors and soil nutrients explained 68% of leaf N:P variation.The P limitation exhibited by tree species in eastern subtropical forest ecosystems intensified over time,and elevated temperature and CO_(2) levels,coupled with decreased soil available P concentrations,appear to be the main factors driving long-term leaf N:P increases in these forests.Investigating long-term variations in soil nutrients together with global change factors will improve our understanding of the nutrient status of forest ecosystems in the context of global change and will support effective forest ecosystem management.展开更多
Investigating the effects of ontogenetic stage and leaf age on leaf traits is important for understanding the utilization and distribution of resources in the process of plant growth.However,few studies have been cond...Investigating the effects of ontogenetic stage and leaf age on leaf traits is important for understanding the utilization and distribution of resources in the process of plant growth.However,few studies have been conducted to show how traits and trait-trait relationships change across a range of ontogenetic stage and leaf age for evergreen coniferous species.We divided 67 Pinus koraiensis Sieb.et Zucc.of various sizes(0.3-100 cm diameter at breast height,DBH)into four ontogenetic stages,i.e.,young trees,middle-aged trees,mature trees and over-mature trees,and measured the leaf mass per area(LMA),leaf dry matter content(LDMC),and mass-based leaf nitrogen content(N)and phosphorus content(P)of each leaf age group for each sampled tree.One-way analysis of variance(ANOVA)was used to describe the variation in leaf traits by ontogenetic stage and leaf age.The standardized major axis method was used to explore the effects of ontogenetic stage and leaf age on trait-trait relationships.We found that LMA and LDMC increased significantly and N and P decreased significantly with increases in the ontogenetic stage and leaf age.Most trait-trait relationships were consistent with the leaf economic spectrum(LES)at a global scale.Among them,leaf N content and LDMC showed a significant negative correlation,leaf N and P contents showed a significant positive correlation,and the absolute value of the slopes of the trait-trait relationships showed a gradually increasing trend with an increasing ontogenetic stage.LMA and LDMC showed a significant positive correlation,and the slopes of the trait-trait relationships showed a gradually decreasing trend with leaf age.Additionally,there were no significant relationships between leaf N content and LMA in most groups,which is contrary to the expectation of the LES.Overall,in the early ontogenetic stages and leaf ages,the leaf traits tend to be related to a"low investment-quick returns"resource strategy.In contrast,in the late ontogenetic stages and leaf ages,they tend to be related to a"high investment-slow returns"resource strategy.Our results reflect the optimal allocation of resources in Pinus koraiensis according to its functional needs during tree and leaf ontogeny.展开更多
Brassica napus(oilseed rape)is sensitive to boron(B)deficiency and exhibits young leaf curling in response to low-B stress at the seedling stage,which leads to reduced photosynthesis and plant growth.So far,no gene ha...Brassica napus(oilseed rape)is sensitive to boron(B)deficiency and exhibits young leaf curling in response to low-B stress at the seedling stage,which leads to reduced photosynthesis and plant growth.So far,no gene has been identified to be involved in B deficiency induced leaf curling.Our previous results showed the transcription factor BnaA1.WRKY53 might be involved in B-deficiency tolerance.However,altered BnaA1.WRKY53 expression does not influence B concentration in shoot,root and leaf cell walls,which suggests Bna A1.WRKY53 might be involved in other biological processes.Indeed,phenotypic and anatomical analyses revealed that BnaA1.WRKY53 negatively regulated the leaf curling induced by leaf epinasty by suppressing the overexpansion of palisade cells under B deficiency.Further transcriptome enrichment analysis of differentially expressed genes(DEGs)between wild-type and BnaA1.WRKY53overexpression line showed auxin response pathway was enriched.In addition,Arabidopsis DR5::GFP auxin reporter line showed B deficiency caused predominant auxin signal accumulation in the adaxial side and concomitant adaxial cell expansion,which indicated that B deficiency may induce leaf curling by altering auxin distribution.Phytohormone quantification and gene expression analysis demonstrated that BnaA1.WRKY53 prevent auxin overaccumulation in leaves by suppressing auxin biosynthetic genes under B deficiency.Furthermore,exogenous 1-naphthlcetic acid(NAA)treatment experiments revealed that high auxin could induce leaf curling and BnaA1.WRKY53 expression.Overall,these findings demonstrate that auxin and the transcription factor BnaA1.WRKY53 synergistically regulate leaf curling to maintain an optimal leaf area under B deficiency,and provide novel insights into the resistance mechanisms against B-deficiency-induced leaf curling in oilseed rape.展开更多
Palm leaf weaving is a traditional Chinese craft.People make different kinds of tools and things with palm leaves.At school,there is a palm leaf weaving club.Would you like to join?
Wheat leaf rust,caused by Puccinia triticina(Pt),is one of the most devastating diseases in common wheat(Triticum aestivum L.)and can lead to heavy yield loss(Chai et al.2020).Leaf rust can result in 50%yield loss dur...Wheat leaf rust,caused by Puccinia triticina(Pt),is one of the most devastating diseases in common wheat(Triticum aestivum L.)and can lead to heavy yield loss(Chai et al.2020).Leaf rust can result in 50%yield loss during epidemic years(Huerta-Espino et al.2011;Gebrewahid et al.2020;Kolomiets et al.2021).Breeding varieties resistant to leaf rust have been recognized as the most effective and economical method to mitigate wheat losses caused by Pt.The narrow genetic basis of wheat constrains the number of cultivars resistant to leaf rust(Jin et al.2021).展开更多
Potassium(K)is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution,indirectly leading to variations in the leaf potassium content(LKC,%)...Potassium(K)is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution,indirectly leading to variations in the leaf potassium content(LKC,%)at different leaf positions.However,due to the interaction between light and leaf age,leaf sensitivity to this change varies at different positions,including the reflection and absorption of the spectrum.Selecting the optimal leaf position for monitoring is a crucial factor in the rapid and accurate evaluation of cotton LKC using spectral remote sensing technology.Therefore,this study proposes a comprehensive multi-leaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom,aiming to achieve an accurate estimation of cotton LKC and optimize the strategy for selecting the monitored leaf position.Between 2020 and 2021,we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom(Li,i=1,2,3,...,n)during the cotton budding,flowering,and boll-setting stages.Vertical distribution characteristics,sensitivity differences,and spectral correlations of LKC at different leaf positions were investigated.Additionally,the optimal range of the dominant leaf position for monitoring was determined.Partial least squares regression(PLSR),random forest regression(RFR),support vector machine regression(SVR),and the entropy weight method(EWM)were employed to develop LKC estimation models for single-and multi-leaf positions.The results showed a vertical heterogeneous distribution of cotton LKC,with LKC initially increasing and then gradually decreasing from top to bottom;the average LKC of cotton reached its maximum value at the flowering stage.The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum.The selected dominant leaf positions for the three growth stages were L1-L5,L1-L4,and L1-L2,respectively.Based on the dominant leaf position monitoring range,the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4,PLSR-L1,and SVR-L2,with the coefficient of determination of the validation set(R2val)being 0.786,0.580,and 0.768,and the root-mean-square error of the validation set(RMSEval)being 0.168,0.197,and 0.191,respectively.The multi-leaf position LKC estimation model was constructed by EWM with R2val being 0.887,0.728,and 0.703,and RMSEval being 0.134,0.172,and 0.209,respectively.In contrast,the newly developed multi-leaf position comprehensive estimation model yielded superior results,improving the model’s stability based on high accuracy,especially during the budding and flowering stages.These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.展开更多
The leaf is a major organ for photosynthesis,and its shape plays an important role in plant development and yield determination in rice(Oryza sativa L.).In this study,an adaxial curled leaf mutant,termed curly leaf 1-...The leaf is a major organ for photosynthesis,and its shape plays an important role in plant development and yield determination in rice(Oryza sativa L.).In this study,an adaxial curled leaf mutant,termed curly leaf 1-1(cul1-1),was obtained by chemical mutagenesis.The leaf rolling index of the cul1-1 mutant was higher than that of the wild-type,which was caused by the abnormal development of bulliform cells(BCs).We cloned the CUL1 gene by map-based cloning.A nonsense mutation was present in the cul1-1 mutant,converting a tryptophan codon into a stop codon.The CUL1 gene encodes a chromodomain,helicase/ATPase and DNA-binding domain containing protein.Genes related to leaf rolling and BC development,such as ADL1,REL1 and ROC5,were activated by the cul1-1 mutation.The trimethylation of lysine 27 in histone 3(H3K27me3),but not H3K4me3,at the ADL1,REL1 and ROC5 loci,was reduced in the cul1-1 mutant.High-throughput mRNA sequencing indicated that the cul1-1 mutation caused genome-wide differential gene expression.The differentially expressed genes were classified into a few gene ontology terms and Kyoto encyclopedia of genes and genomes pathways.In the natural population,22 missense genomic variations in the CUL1 locus were identified,which composed of 7 haplotypes.A haplotype network was also built with haplotype II as the ancestor.The findings revealed that CUL1 is essential for normal leaf development and regulates this process by inhibiting the expression of genes involved in leaf rolling and BC development.展开更多
Flag leaf angle(FLANG)is one of the key traits in wheat breeding due to its impact on plant architecture,light interception,and yield potential.An image-based method of measuring FLANG in wheat would reduce the labor ...Flag leaf angle(FLANG)is one of the key traits in wheat breeding due to its impact on plant architecture,light interception,and yield potential.An image-based method of measuring FLANG in wheat would reduce the labor and error of manual measurement of this trait.We describe a method for acquiring in-field FLANG images and a lightweight deep learning model named LeafPoseNet that incorporates a spatial attention mechanism for FLANG estimation.In a test dataset with wheat varieties exhibiting diverse FLANG,LeafPoseNet achieved high accuracy in predicting the FLANG,with a mean absolute error(MAE)of 1.75°,a root mean square error(RMSE)of 2.17°,and a coefficient of determination(R2)of 0.998,significantly outperforming established models such as YOLO12x-pose,YOLO11x-pose,HigherHRNet,Lightweight-OpenPose,and LitePose.We performed phenotyping and genome-wide association study to identify the genomic regions associated with FLANG in a panel of 221 diverse bread wheat genotypes,and identified 10 quantitative trait loci.Among them,qFLANG2B.2 was found to harbor a potential causal gene,TraesCS2B01G313700,which may regulate FLANG formation by modulating brassinosteroid levels.This method provides a low-cost,high-accuracy solution for in-field phenotyping of wheat FLANG,facilitating both wheat FLANG genetic studies and ideal plant type breeding.展开更多
Agromyzid leafminers cause significant economic losses in both vegetable and horticultural crops,and precise assessments of pesticide needs must be based on the extent of leaf damage.Traditionally,surveyors estimate t...Agromyzid leafminers cause significant economic losses in both vegetable and horticultural crops,and precise assessments of pesticide needs must be based on the extent of leaf damage.Traditionally,surveyors estimate the damage by visually comparing the proportion of damaged to intact leaf area,a method that lacks objectivity,precision,and reliable data traceability.To address these issues,an advanced survey system that combines augmented reality(AR)glasses with a camera and an artificial intelligence(AI)algorithm was developed in this study to objectively and accurately assess leafminer damage in the feld.By wearing AR glasses equipped with a voice-controlled camera,surveyors can easily flatten damaged leaves by hand and capture images for analysis.This method can provide a precise and reliable diagnosis of leafminer damage levels,which in turn supports the implementation of scientifically grounded and targeted pest management strategies.To calculate the leafminer damage level,the DeepLab-Leafminer model was proposed to precisely segment the leafminer-damaged regions and the intact leaf region.The integration of an edge-aware module and a Canny loss function into the DeepLabv3+model enhanced the DeepLab-Leafminer model's capability to accurately segment the edges of leafminer-damaged regions,which often exhibit irregular shapes.Compared with state-of-the-art segmentation models,the DeepLabLeafminer model achieved superior segmentation performance with an Intersection over Union(IoU)of 81.23%and an F1score of 87.92%on leafminer-damaged leaves.The test results revealed a 92.38%diagnosis accuracy of leafminer damage levels based on the DeepLab-Leafminer model.A mobile application and a web platform were developed to assist surveyors in displaying the diagnostic results of leafminer damage levels.This system provides surveyors with an advanced,user-friendly,and accurate tool for assessing agromyzid leafminer damage in agricultural felds using wearable AR glasses and an AI model.This method can also be utilized to automatically diagnose pest and disease damage levels in other crops based on leaf images.展开更多
The Agrobacterium-mediated transient expression system with conventional binary vectors is well established in tobacco leaves,while the same system applied to tomato leaflets has relatively low expression efficiency.H...The Agrobacterium-mediated transient expression system with conventional binary vectors is well established in tobacco leaves,while the same system applied to tomato leaflets has relatively low expression efficiency.However,impacts of the leaf age,inoculation method and incubation condition after Agrobacterium infiltration on transient protein expression efficiency are seldom investigated.In this study,we optimize Agrobacterium-mediated transient expression system using conventional binary vectors to achieve the high efficiency of target gene expression in tomato leaflets.We transiently express GFP and a nucleus-localized gene SlUVI4 fused with GFP in detached 10-,20-,and 30-day-old leaflets.The cutting points of leaflets are embedded in MS medium after the Agrobacterium-mediated vacuum infiltration,and all leaflets are kept in the dark before use.The 10-and 30-day-old leaflets have more damage than 20-day-old leaflets after the infiltration.展开更多
[Objective] The paper was to study the effect of tobacco blown spot on the yield and output value of tobacco leaf.[Method]The upper,middle and lower leaves in tobacco plant were selected during the harvest period of t...[Objective] The paper was to study the effect of tobacco blown spot on the yield and output value of tobacco leaf.[Method]The upper,middle and lower leaves in tobacco plant were selected during the harvest period of tobacco to carry out loss rate estimation of yield and output value of tobacco leaf caused by different disease levels of brown spot.Regression correlation analysis was also conducted.[Result]The disease levels of brown spot had extremely significant strong negative correlation with single leaf weight of tobacco leaf,and it had extremely significant strong positive correlation with the loss rate of single leaf weight.The increase speed of loss rate of single leaf weight of middle and upper leaves was obviously faster than that of lower leaves.The loss rates of single leaf weight of upper,middle and lower leaves were 3.18%-28.95%,3.43%-28.88% and 10.07%-26.90%,respectively.The higher the disease level of blown spot was,the lower the yield and output value of tobacco leaf was,and the corresponding loss rate was also higher.Correlation analysis showed that the disease level of blown spot had extremely significant strong negative correlation with the yield and output value of tobacco leaf,and it had extremely significant strong positive correlation with the loss rate of yield and output value.The negative impact of blown spot on the output value of tobacco leaf was far greater than that on the yield.The highest loss rate of the yield of tobacco leaf was 28.56%,while the highest loss rate of output value reached 89.67%.[Conclusion] The study provided theoretical basis for accurately holding the critical period for the control of blown spot,thus reducing the damage on tobacco leaf and improving the output value of tobacco leaf.展开更多
The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non...The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.展开更多
DNA methylation plays important roles in regulating gene expression during development.However,little is known about the influence of DNA methylation on secondary metabolism during leaf development in the tea plant(Ca...DNA methylation plays important roles in regulating gene expression during development.However,little is known about the influence of DNA methylation on secondary metabolism during leaf development in the tea plant(Camellia sinensis).In this study,we combined the methylome,transcriptome,and metabolome to investigate the dynamic changes in DNA methylation and its potential regulatory roles in secondary metabolite biosynthesis.In this study,the level of genomic DNA methylation increased as leaf development progressed from tender to old leaf.It additionally exhibited a similar distribution across the genomic background at the two distinct developmental stages studied.Notably,integrated analysis of transcriptomic and methylomic data showed that DNA hypermethylation primarily occurred in genes of the phenylpropanoid,flavonoid,and terpenoid biosynthesis pathways.The effect of methylation on transcription of these secondary metabolite biosynthesis genes was dependent on the location of methylation(i.e.,in the promoter,gene or intergenic regions)and the sequence context(i.e.,CpG,CHG,or CHH).Changes in the content of catechins and terpenoids were consistent with the changes in gene transcription and the methylation state of structural genes,such as serine carboxypeptidase-like acyltransferases 1A(SCPL1A),leucoanthocyanidin reductase(LAR),and nerolidol synthase(NES).Our study provides valuable information for dissecting the effects of DNA methylation on regulation of genes involved in secondary metabolism during tea leaf development.展开更多
The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,...The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,and the network pharmacology was used to elucidate their anti-hair loss mechanism,which was verified by molecular docking technology.572 active compounds were identified from the ASE by metabolomics methods,where there are 1447 corresponding targets and 492 targets related to hair loss,totaling 88 targets.20 core active substances were identified by constructing a network between common targets and active substances,which include vanillic acid,chorionic acid,caffeic acid and apigenin.The five key targets of TNF,TP53,IL6,PPARG,and EGFR were screened out by the PPI network analysis on 88 common targets.The GO and KEGG pathway enrichment analysis showed that the inflammation,hormone balance,cell growth,proliferation,apoptosis,and oxidative stress are involved.Molecular docking studies have confirmed the high binding affinity between core active compounds and key targets.The drug similarity assessment on these core compounds suggested that they have the potential to be used as potential hair loss treatment drugs.This study elucidates the complex molecular mechanism of ASE in treating hair loss,and provides a reference for the future applications in hair care products.展开更多
Leaf area is an important parameter for modeling tree growth and physiological processes of trees. The single young and mature leaf area estimation models of eucalyptus were developed based on leaf fresh weight. In to...Leaf area is an important parameter for modeling tree growth and physiological processes of trees. The single young and mature leaf area estimation models of eucalyptus were developed based on leaf fresh weight. In total, leaf area and leaf weight were measured from 455 fresh leaves of 25 trees of eucalyptus in southern China. The majority of the data （80%） were used for model calibration, and the remaining data （20%） were used for model validation. The linear, compound and power models were tested. Based on goodness of fit, prediction ability and residual performance, we found that linear and power models could best describe the relationship between leaf area and weight for young leaf and mature leaf, respectively. The study provides a simple and reliable method for estimating single-leaf area, which has a good potential in the functional- structural model of eucalyptus.展开更多
This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot ...This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot taken from a top-down perspective at a distance of 30 cm. These samples were projected into the CIELAB color space, and the extracted pixels were plotted on the a*b* plane, excluding the luminance value. A polygon was then drawn around all the plotted pixels, defining the color to be identified. Subsequently, the application analyzed another image to determine the number of pixels within the polygon. These identified pixels were transformed to white, and the percentage of these pixels relative to the total number of pixels in the image was calculated. This process yielded percentages for brown (soil), green (leaf cover), and pink (stem color). A single polygon was sufficient to accurately identify the green and brown colors in the images. However, due to varying lighting conditions, customized polygons were necessary for each image to accurately identify the stem color. To validate the green polygon’s accuracy in identifying purslane leaves, all leaves in the image were digitized in AutoCADTM, and the green area was compared to the total image area to obtain the observed green percentage. The green percentage obtained with the polygon was then compared to the observed green percentage, resulting in an R2 value of 0.8431. Similarly, for the brown color, an R2 value of 0.9305 was found. The stem color was not subjected to this validation due to the necessity of multiple polygons. The R2 values were derived from percentage data obtained by analyzing the total pixels in the images. When sampling to estimate the proportion and analyzing only the suggested sample size of pixels, R2 values of 0.93049 for brown and 0.8088 for green were obtained. The average analysis time to determine the brown soil percentage using the polygon (BP) for 26 images with an average size of 1070 × 1210 pixels was 44 seconds. In contrast, sampling to estimate the proportion reduced the analysis time to 0.9 seconds for the same number of images. This indicates that significant time savings can be achieved while obtaining similar results.展开更多
Two leaf color variants red-leaf(R-type)and common-leaf(G-type)of Euonymus sacrosancta Koidz.,were employed as experimental materials to elucidate the molecular mechanisms underlying chromatic transition.Physiological...Two leaf color variants red-leaf(R-type)and common-leaf(G-type)of Euonymus sacrosancta Koidz.,were employed as experimental materials to elucidate the molecular mechanisms underlying chromatic transition.Physiological profiling identified anthocyanins and flavo-noids as the predominant pigments responsible for the red foliar phenotype,which exhibited reduced chlorophyll and carotenoid accumulation but elevated soluble sugars and proteins.Comparative transcriptomic analysis revealed that differentially expressed genes(DEGs)between R-type and G-type were significantly enriched in flavonoid biosynthe-sis and carotenoid metabolism pathways.The up-regulation of 22 key genes of anthocyanin synthesis(e.g.,CHS,CHI,LAR,LDOX and UFGT)in R-type may lead to the phenotype of red leaves through the increase of anthocyanin accumula-tion.The downregulated expression of 13 carotenoid syn-thesis-related genes(e.g.,PSY,PDS and VDE)and 6 carot-enoid degradation genes(e.g.,ABA2,CYP707A and NCED)may lead to lower carotenoid content in R-type compared to G-type.Combined with weighted gene co-expression network analysis(WGCNA),five candidate genes(EsLAR,EsLDOX,EsPDS,EsCYP707A and EsABA2)were screened from two modules highly correlated with anthocyanin con-tent in E.sacrosancta leaves.These genes may play key regulatory roles in leaf coloration and could serve as candi-date genetic resources for leaf color improvement in E.sac-rosancta.Additionally,transcription factors such as C2H2s,C3Hs,and WRKYs were identified as potential regulators in the formation of R-type in E.sacrosancta.This study pro-vides the first systematic elucidation of the transcriptional regulatory network governing red-leaf formation in E.sac-rosancta,establishing a critical theoretical foundation for molecular breeding in ornamental plants.展开更多
Rapid urbanization has contributed to global increases in air pollution derived from urban areas.Unlike natural forests,urban forests are exposed to higher concentrations of airborne pollutants due to the strong urban...Rapid urbanization has contributed to global increases in air pollution derived from urban areas.Unlike natural forests,urban forests are exposed to higher concentrations of airborne pollutants due to the strong urban-suburban-rural pollutant emission gradients.However,there remains a pressing lack of available information pertaining to the urban air pollutionrelated effects on the leaf economics spectrum,anatomical,ultrastructural,and stomatal traits of tree species along an urban-rural gradient.Here,the degree to which urban air pollution impacts the adaption of greening tree species and associated service functions was assessed by sampling five common tree species(Acer pictum,Fraxinus chinensis,Koelreuteria paniculata,Salix babylonica,Sophora japonica)along urban-rural-natural forests in the Beijing metropolitan region of China.These analyses revealed a significant reduction in leaf mass per unit area(-13.4%),leaf thickness(-16.7%),and stomatal area(-27.5%)with increasing proximity to areas of greater air pollution that coincide with significant increases in leaf tissue density(+12.6%),leaf nitrogen content(+10.1%),relative chlorophyll content(+2.7%),and stomatal density(+11.9%).Higher air pollution levels were associated with organelle changes including gradual disintegration of chloroplasts,larger intercellular spaces and apparent starch and plastoglobuli deposition.Air pollutionwas conducive to the strengthening of the trade-off potential and adaptation strategies of trees in urban ecosystems,which are associated with trees with a rapid investment return strategy associated with thick leaves and strong photosynthetic capacity.These results provide strong empirical evidence of the profound air pollution-induced changes in leaf functional traits and adaption ability of urban forest tree species.展开更多
Sunflower leaf photosynthesis strongly depends on the leaf position in the plant stem conditioning,which directly affects other physiological processes.Therefore,a study of the leaf’s physiological status regarding t...Sunflower leaf photosynthesis strongly depends on the leaf position in the plant stem conditioning,which directly affects other physiological processes.Therefore,a study of the leaf’s physiological status regarding the leaf position in the stem was performed on sunflowers in the flowering stage.Eight differently positioned leaves were investigated,starting with the youngest leaf on the top of the stem to the leaves of the stem bottom,assigned as the oldest senescent leaves.According to chlorophyll fluorescence(ChlF)parameters connected to photosystem II(PSII)processes,significant changes in PSII functioning occurred only in the senescent leaves,while photosystem I(PSI)describing parameters showed a linear decrease with leaf age,i.e.,position on the stem.The antioxidative status of the leaves was dynamic,as stress indicators(lipid peroxidation and hydrogen peroxide content)fluctuated regarding leaf position on the stem,but no link was found between the activities of antioxidative enzymes and oxidative stress indicators.Linear decrease trend of secondary metabolites(mainly phenolic compounds)correlated with antioxidant activity,except for some phenolic acids(caffeic and ferulic acid),which increased in senescent leaves.The most changes in the physiological status of the leaves were confirmed in senescent leaves,which stand out the importance of younger leaves in maintaining the plant’s vitality after flowering,which is the most important for sunflower yield.展开更多
基金supported by the National Natural Science Foundation of China(No.42030509)the Special Project on National Science and Technology Basic Resources Investigation of China(No.2021FY100705).
文摘The leaf nitrogen(N)to phosphorus(P)ratio(N:P)is a critical indicator of nutrient dynamics and ecosystem function.Investigating temporal variations in leaf N:P can provide valuable insights into how plants adapt to environmental changes and nutrient availability.However,limited research has been conducted on long-term temporal leaf N:P variation over a range of temperature zones.Using long-term monitoring data from the Chinese Ecosystem Research Network(CERN),we investigated temporal changes in leaf N and P stoichiometry for 50 dominant tree species from 10 typical forest sites across temperate and subtropical regions,and identified the underlying mechanisms driving these changes.For both regions combined,leaf P concentration of the 50 dominant tree species decreased(20.6%),whereas leaf N:P increased(52.0%)from 2005 to 2020.Leaf P decreased and leaf N:P increased in 67% and 69% of the tree species,respectively.The leaf N:P increase was primarily driven by the tree species in eastern subtropical forests,where global change factors and soil nutrients explained 68% of leaf N:P variation.The P limitation exhibited by tree species in eastern subtropical forest ecosystems intensified over time,and elevated temperature and CO_(2) levels,coupled with decreased soil available P concentrations,appear to be the main factors driving long-term leaf N:P increases in these forests.Investigating long-term variations in soil nutrients together with global change factors will improve our understanding of the nutrient status of forest ecosystems in the context of global change and will support effective forest ecosystem management.
基金The work was financially supported by the National Natural Science Foundation of China(No.31971636)Yong Elite Scientists Sponsorship Program by CAST(2018QNRC001)the Fundamental Research Fund for the Central Universities(2572018CG03).
文摘Investigating the effects of ontogenetic stage and leaf age on leaf traits is important for understanding the utilization and distribution of resources in the process of plant growth.However,few studies have been conducted to show how traits and trait-trait relationships change across a range of ontogenetic stage and leaf age for evergreen coniferous species.We divided 67 Pinus koraiensis Sieb.et Zucc.of various sizes(0.3-100 cm diameter at breast height,DBH)into four ontogenetic stages,i.e.,young trees,middle-aged trees,mature trees and over-mature trees,and measured the leaf mass per area(LMA),leaf dry matter content(LDMC),and mass-based leaf nitrogen content(N)and phosphorus content(P)of each leaf age group for each sampled tree.One-way analysis of variance(ANOVA)was used to describe the variation in leaf traits by ontogenetic stage and leaf age.The standardized major axis method was used to explore the effects of ontogenetic stage and leaf age on trait-trait relationships.We found that LMA and LDMC increased significantly and N and P decreased significantly with increases in the ontogenetic stage and leaf age.Most trait-trait relationships were consistent with the leaf economic spectrum(LES)at a global scale.Among them,leaf N content and LDMC showed a significant negative correlation,leaf N and P contents showed a significant positive correlation,and the absolute value of the slopes of the trait-trait relationships showed a gradually increasing trend with an increasing ontogenetic stage.LMA and LDMC showed a significant positive correlation,and the slopes of the trait-trait relationships showed a gradually decreasing trend with leaf age.Additionally,there were no significant relationships between leaf N content and LMA in most groups,which is contrary to the expectation of the LES.Overall,in the early ontogenetic stages and leaf ages,the leaf traits tend to be related to a"low investment-quick returns"resource strategy.In contrast,in the late ontogenetic stages and leaf ages,they tend to be related to a"high investment-slow returns"resource strategy.Our results reflect the optimal allocation of resources in Pinus koraiensis according to its functional needs during tree and leaf ontogeny.
基金supported by the National Natural Science Foundation of China(32002122,32372805)。
文摘Brassica napus(oilseed rape)is sensitive to boron(B)deficiency and exhibits young leaf curling in response to low-B stress at the seedling stage,which leads to reduced photosynthesis and plant growth.So far,no gene has been identified to be involved in B deficiency induced leaf curling.Our previous results showed the transcription factor BnaA1.WRKY53 might be involved in B-deficiency tolerance.However,altered BnaA1.WRKY53 expression does not influence B concentration in shoot,root and leaf cell walls,which suggests Bna A1.WRKY53 might be involved in other biological processes.Indeed,phenotypic and anatomical analyses revealed that BnaA1.WRKY53 negatively regulated the leaf curling induced by leaf epinasty by suppressing the overexpansion of palisade cells under B deficiency.Further transcriptome enrichment analysis of differentially expressed genes(DEGs)between wild-type and BnaA1.WRKY53overexpression line showed auxin response pathway was enriched.In addition,Arabidopsis DR5::GFP auxin reporter line showed B deficiency caused predominant auxin signal accumulation in the adaxial side and concomitant adaxial cell expansion,which indicated that B deficiency may induce leaf curling by altering auxin distribution.Phytohormone quantification and gene expression analysis demonstrated that BnaA1.WRKY53 prevent auxin overaccumulation in leaves by suppressing auxin biosynthetic genes under B deficiency.Furthermore,exogenous 1-naphthlcetic acid(NAA)treatment experiments revealed that high auxin could induce leaf curling and BnaA1.WRKY53 expression.Overall,these findings demonstrate that auxin and the transcription factor BnaA1.WRKY53 synergistically regulate leaf curling to maintain an optimal leaf area under B deficiency,and provide novel insights into the resistance mechanisms against B-deficiency-induced leaf curling in oilseed rape.
文摘Palm leaf weaving is a traditional Chinese craft.People make different kinds of tools and things with palm leaves.At school,there is a palm leaf weaving club.Would you like to join?
基金funded by the National Natural Science Foundation of China(32272083)。
文摘Wheat leaf rust,caused by Puccinia triticina(Pt),is one of the most devastating diseases in common wheat(Triticum aestivum L.)and can lead to heavy yield loss(Chai et al.2020).Leaf rust can result in 50%yield loss during epidemic years(Huerta-Espino et al.2011;Gebrewahid et al.2020;Kolomiets et al.2021).Breeding varieties resistant to leaf rust have been recognized as the most effective and economical method to mitigate wheat losses caused by Pt.The narrow genetic basis of wheat constrains the number of cultivars resistant to leaf rust(Jin et al.2021).
基金supported by the Corps Leading Talents Program,China(2023YZ01)the Tianshan Talent Training Program,China(2023TS05)the Crop Smart Production Innovation Team,China(2023TD01).
文摘Potassium(K)is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution,indirectly leading to variations in the leaf potassium content(LKC,%)at different leaf positions.However,due to the interaction between light and leaf age,leaf sensitivity to this change varies at different positions,including the reflection and absorption of the spectrum.Selecting the optimal leaf position for monitoring is a crucial factor in the rapid and accurate evaluation of cotton LKC using spectral remote sensing technology.Therefore,this study proposes a comprehensive multi-leaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom,aiming to achieve an accurate estimation of cotton LKC and optimize the strategy for selecting the monitored leaf position.Between 2020 and 2021,we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom(Li,i=1,2,3,...,n)during the cotton budding,flowering,and boll-setting stages.Vertical distribution characteristics,sensitivity differences,and spectral correlations of LKC at different leaf positions were investigated.Additionally,the optimal range of the dominant leaf position for monitoring was determined.Partial least squares regression(PLSR),random forest regression(RFR),support vector machine regression(SVR),and the entropy weight method(EWM)were employed to develop LKC estimation models for single-and multi-leaf positions.The results showed a vertical heterogeneous distribution of cotton LKC,with LKC initially increasing and then gradually decreasing from top to bottom;the average LKC of cotton reached its maximum value at the flowering stage.The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum.The selected dominant leaf positions for the three growth stages were L1-L5,L1-L4,and L1-L2,respectively.Based on the dominant leaf position monitoring range,the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4,PLSR-L1,and SVR-L2,with the coefficient of determination of the validation set(R2val)being 0.786,0.580,and 0.768,and the root-mean-square error of the validation set(RMSEval)being 0.168,0.197,and 0.191,respectively.The multi-leaf position LKC estimation model was constructed by EWM with R2val being 0.887,0.728,and 0.703,and RMSEval being 0.134,0.172,and 0.209,respectively.In contrast,the newly developed multi-leaf position comprehensive estimation model yielded superior results,improving the model’s stability based on high accuracy,especially during the budding and flowering stages.These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.
基金supported by the National Natural Science Foundation of China(32070642 and 31371222 to Dr.Xiaoxue Wang)the National Key Research and Development Program from the Ministry of Science and Technology of China(2016YFD0100406 and 2017YFD0300107 to Dr.Xiaoxue Wang)the Science and Technology Department of Liaoning province(2022JH6/100100039 to Dr.Xiaoxue Wang)。
文摘The leaf is a major organ for photosynthesis,and its shape plays an important role in plant development and yield determination in rice(Oryza sativa L.).In this study,an adaxial curled leaf mutant,termed curly leaf 1-1(cul1-1),was obtained by chemical mutagenesis.The leaf rolling index of the cul1-1 mutant was higher than that of the wild-type,which was caused by the abnormal development of bulliform cells(BCs).We cloned the CUL1 gene by map-based cloning.A nonsense mutation was present in the cul1-1 mutant,converting a tryptophan codon into a stop codon.The CUL1 gene encodes a chromodomain,helicase/ATPase and DNA-binding domain containing protein.Genes related to leaf rolling and BC development,such as ADL1,REL1 and ROC5,were activated by the cul1-1 mutation.The trimethylation of lysine 27 in histone 3(H3K27me3),but not H3K4me3,at the ADL1,REL1 and ROC5 loci,was reduced in the cul1-1 mutant.High-throughput mRNA sequencing indicated that the cul1-1 mutation caused genome-wide differential gene expression.The differentially expressed genes were classified into a few gene ontology terms and Kyoto encyclopedia of genes and genomes pathways.In the natural population,22 missense genomic variations in the CUL1 locus were identified,which composed of 7 haplotypes.A haplotype network was also built with haplotype II as the ancestor.The findings revealed that CUL1 is essential for normal leaf development and regulates this process by inhibiting the expression of genes involved in leaf rolling and BC development.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04076)the National Key Research and Development Program of China(2023YFF1000100)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA0450000).
文摘Flag leaf angle(FLANG)is one of the key traits in wheat breeding due to its impact on plant architecture,light interception,and yield potential.An image-based method of measuring FLANG in wheat would reduce the labor and error of manual measurement of this trait.We describe a method for acquiring in-field FLANG images and a lightweight deep learning model named LeafPoseNet that incorporates a spatial attention mechanism for FLANG estimation.In a test dataset with wheat varieties exhibiting diverse FLANG,LeafPoseNet achieved high accuracy in predicting the FLANG,with a mean absolute error(MAE)of 1.75°,a root mean square error(RMSE)of 2.17°,and a coefficient of determination(R2)of 0.998,significantly outperforming established models such as YOLO12x-pose,YOLO11x-pose,HigherHRNet,Lightweight-OpenPose,and LitePose.We performed phenotyping and genome-wide association study to identify the genomic regions associated with FLANG in a panel of 221 diverse bread wheat genotypes,and identified 10 quantitative trait loci.Among them,qFLANG2B.2 was found to harbor a potential causal gene,TraesCS2B01G313700,which may regulate FLANG formation by modulating brassinosteroid levels.This method provides a low-cost,high-accuracy solution for in-field phenotyping of wheat FLANG,facilitating both wheat FLANG genetic studies and ideal plant type breeding.
基金supported by the National Key R&D Program of China(2021YFC2600400 and 2023YFC2605200)the National Key Research Program of China(2021YFD1401100)the“San Nong Jiu Fang”Sciences and Technologies Cooperation Project of Zhejiang Province,China(2024SNJF010)。
文摘Agromyzid leafminers cause significant economic losses in both vegetable and horticultural crops,and precise assessments of pesticide needs must be based on the extent of leaf damage.Traditionally,surveyors estimate the damage by visually comparing the proportion of damaged to intact leaf area,a method that lacks objectivity,precision,and reliable data traceability.To address these issues,an advanced survey system that combines augmented reality(AR)glasses with a camera and an artificial intelligence(AI)algorithm was developed in this study to objectively and accurately assess leafminer damage in the feld.By wearing AR glasses equipped with a voice-controlled camera,surveyors can easily flatten damaged leaves by hand and capture images for analysis.This method can provide a precise and reliable diagnosis of leafminer damage levels,which in turn supports the implementation of scientifically grounded and targeted pest management strategies.To calculate the leafminer damage level,the DeepLab-Leafminer model was proposed to precisely segment the leafminer-damaged regions and the intact leaf region.The integration of an edge-aware module and a Canny loss function into the DeepLabv3+model enhanced the DeepLab-Leafminer model's capability to accurately segment the edges of leafminer-damaged regions,which often exhibit irregular shapes.Compared with state-of-the-art segmentation models,the DeepLabLeafminer model achieved superior segmentation performance with an Intersection over Union(IoU)of 81.23%and an F1score of 87.92%on leafminer-damaged leaves.The test results revealed a 92.38%diagnosis accuracy of leafminer damage levels based on the DeepLab-Leafminer model.A mobile application and a web platform were developed to assist surveyors in displaying the diagnostic results of leafminer damage levels.This system provides surveyors with an advanced,user-friendly,and accurate tool for assessing agromyzid leafminer damage in agricultural felds using wearable AR glasses and an AI model.This method can also be utilized to automatically diagnose pest and disease damage levels in other crops based on leaf images.
基金support of Taishan Scholar Foundation of Shandong Province(tsqn201909073,tsqn201812034)National Natural Science Foundation of China(31872951)。
文摘The Agrobacterium-mediated transient expression system with conventional binary vectors is well established in tobacco leaves,while the same system applied to tomato leaflets has relatively low expression efficiency.However,impacts of the leaf age,inoculation method and incubation condition after Agrobacterium infiltration on transient protein expression efficiency are seldom investigated.In this study,we optimize Agrobacterium-mediated transient expression system using conventional binary vectors to achieve the high efficiency of target gene expression in tomato leaflets.We transiently express GFP and a nucleus-localized gene SlUVI4 fused with GFP in detached 10-,20-,and 30-day-old leaflets.The cutting points of leaflets are embedded in MS medium after the Agrobacterium-mediated vacuum infiltration,and all leaflets are kept in the dark before use.The 10-and 30-day-old leaflets have more damage than 20-day-old leaflets after the infiltration.
基金Supported by State Tobacco Monopoly Administration Project "National Survey of Pests in Tobacco" (110200902065)Yunnan Tobacco Monopoly Bureau Technology Project " Investigation of Tobacco Pests in Yunnan Province" (2010YN19)~~
文摘[Objective] The paper was to study the effect of tobacco blown spot on the yield and output value of tobacco leaf.[Method]The upper,middle and lower leaves in tobacco plant were selected during the harvest period of tobacco to carry out loss rate estimation of yield and output value of tobacco leaf caused by different disease levels of brown spot.Regression correlation analysis was also conducted.[Result]The disease levels of brown spot had extremely significant strong negative correlation with single leaf weight of tobacco leaf,and it had extremely significant strong positive correlation with the loss rate of single leaf weight.The increase speed of loss rate of single leaf weight of middle and upper leaves was obviously faster than that of lower leaves.The loss rates of single leaf weight of upper,middle and lower leaves were 3.18%-28.95%,3.43%-28.88% and 10.07%-26.90%,respectively.The higher the disease level of blown spot was,the lower the yield and output value of tobacco leaf was,and the corresponding loss rate was also higher.Correlation analysis showed that the disease level of blown spot had extremely significant strong negative correlation with the yield and output value of tobacco leaf,and it had extremely significant strong positive correlation with the loss rate of yield and output value.The negative impact of blown spot on the output value of tobacco leaf was far greater than that on the yield.The highest loss rate of the yield of tobacco leaf was 28.56%,while the highest loss rate of output value reached 89.67%.[Conclusion] The study provided theoretical basis for accurately holding the critical period for the control of blown spot,thus reducing the damage on tobacco leaf and improving the output value of tobacco leaf.
基金financially supported by the Deanship of Scientific Research,Qassim University,Saudi Arabia for funding the publication of this project.
文摘The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.
基金supported by the Natural Science Foundation of Guangdong Province(Grant Nos.2022A1515111141 and 2023A1515010786)。
文摘DNA methylation plays important roles in regulating gene expression during development.However,little is known about the influence of DNA methylation on secondary metabolism during leaf development in the tea plant(Camellia sinensis).In this study,we combined the methylome,transcriptome,and metabolome to investigate the dynamic changes in DNA methylation and its potential regulatory roles in secondary metabolite biosynthesis.In this study,the level of genomic DNA methylation increased as leaf development progressed from tender to old leaf.It additionally exhibited a similar distribution across the genomic background at the two distinct developmental stages studied.Notably,integrated analysis of transcriptomic and methylomic data showed that DNA hypermethylation primarily occurred in genes of the phenylpropanoid,flavonoid,and terpenoid biosynthesis pathways.The effect of methylation on transcription of these secondary metabolite biosynthesis genes was dependent on the location of methylation(i.e.,in the promoter,gene or intergenic regions)and the sequence context(i.e.,CpG,CHG,or CHH).Changes in the content of catechins and terpenoids were consistent with the changes in gene transcription and the methylation state of structural genes,such as serine carboxypeptidase-like acyltransferases 1A(SCPL1A),leucoanthocyanidin reductase(LAR),and nerolidol synthase(NES).Our study provides valuable information for dissecting the effects of DNA methylation on regulation of genes involved in secondary metabolism during tea leaf development.
文摘The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,and the network pharmacology was used to elucidate their anti-hair loss mechanism,which was verified by molecular docking technology.572 active compounds were identified from the ASE by metabolomics methods,where there are 1447 corresponding targets and 492 targets related to hair loss,totaling 88 targets.20 core active substances were identified by constructing a network between common targets and active substances,which include vanillic acid,chorionic acid,caffeic acid and apigenin.The five key targets of TNF,TP53,IL6,PPARG,and EGFR were screened out by the PPI network analysis on 88 common targets.The GO and KEGG pathway enrichment analysis showed that the inflammation,hormone balance,cell growth,proliferation,apoptosis,and oxidative stress are involved.Molecular docking studies have confirmed the high binding affinity between core active compounds and key targets.The drug similarity assessment on these core compounds suggested that they have the potential to be used as potential hair loss treatment drugs.This study elucidates the complex molecular mechanism of ASE in treating hair loss,and provides a reference for the future applications in hair care products.
文摘Leaf area is an important parameter for modeling tree growth and physiological processes of trees. The single young and mature leaf area estimation models of eucalyptus were developed based on leaf fresh weight. In total, leaf area and leaf weight were measured from 455 fresh leaves of 25 trees of eucalyptus in southern China. The majority of the data （80%） were used for model calibration, and the remaining data （20%） were used for model validation. The linear, compound and power models were tested. Based on goodness of fit, prediction ability and residual performance, we found that linear and power models could best describe the relationship between leaf area and weight for young leaf and mature leaf, respectively. The study provides a simple and reliable method for estimating single-leaf area, which has a good potential in the functional- structural model of eucalyptus.
文摘This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot taken from a top-down perspective at a distance of 30 cm. These samples were projected into the CIELAB color space, and the extracted pixels were plotted on the a*b* plane, excluding the luminance value. A polygon was then drawn around all the plotted pixels, defining the color to be identified. Subsequently, the application analyzed another image to determine the number of pixels within the polygon. These identified pixels were transformed to white, and the percentage of these pixels relative to the total number of pixels in the image was calculated. This process yielded percentages for brown (soil), green (leaf cover), and pink (stem color). A single polygon was sufficient to accurately identify the green and brown colors in the images. However, due to varying lighting conditions, customized polygons were necessary for each image to accurately identify the stem color. To validate the green polygon’s accuracy in identifying purslane leaves, all leaves in the image were digitized in AutoCADTM, and the green area was compared to the total image area to obtain the observed green percentage. The green percentage obtained with the polygon was then compared to the observed green percentage, resulting in an R2 value of 0.8431. Similarly, for the brown color, an R2 value of 0.9305 was found. The stem color was not subjected to this validation due to the necessity of multiple polygons. The R2 values were derived from percentage data obtained by analyzing the total pixels in the images. When sampling to estimate the proportion and analyzing only the suggested sample size of pixels, R2 values of 0.93049 for brown and 0.8088 for green were obtained. The average analysis time to determine the brown soil percentage using the polygon (BP) for 26 images with an average size of 1070 × 1210 pixels was 44 seconds. In contrast, sampling to estimate the proportion reduced the analysis time to 0.9 seconds for the same number of images. This indicates that significant time savings can be achieved while obtaining similar results.
基金supported by the National Natural Science Foundation of China(No.32171738)the National Key Research and Development Program of China(No.2021YFD2200304)the Fundamental Research Funds for the Central Universities(No.2572022DQ08).
文摘Two leaf color variants red-leaf(R-type)and common-leaf(G-type)of Euonymus sacrosancta Koidz.,were employed as experimental materials to elucidate the molecular mechanisms underlying chromatic transition.Physiological profiling identified anthocyanins and flavo-noids as the predominant pigments responsible for the red foliar phenotype,which exhibited reduced chlorophyll and carotenoid accumulation but elevated soluble sugars and proteins.Comparative transcriptomic analysis revealed that differentially expressed genes(DEGs)between R-type and G-type were significantly enriched in flavonoid biosynthe-sis and carotenoid metabolism pathways.The up-regulation of 22 key genes of anthocyanin synthesis(e.g.,CHS,CHI,LAR,LDOX and UFGT)in R-type may lead to the phenotype of red leaves through the increase of anthocyanin accumula-tion.The downregulated expression of 13 carotenoid syn-thesis-related genes(e.g.,PSY,PDS and VDE)and 6 carot-enoid degradation genes(e.g.,ABA2,CYP707A and NCED)may lead to lower carotenoid content in R-type compared to G-type.Combined with weighted gene co-expression network analysis(WGCNA),five candidate genes(EsLAR,EsLDOX,EsPDS,EsCYP707A and EsABA2)were screened from two modules highly correlated with anthocyanin con-tent in E.sacrosancta leaves.These genes may play key regulatory roles in leaf coloration and could serve as candi-date genetic resources for leaf color improvement in E.sac-rosancta.Additionally,transcription factors such as C2H2s,C3Hs,and WRKYs were identified as potential regulators in the formation of R-type in E.sacrosancta.This study pro-vides the first systematic elucidation of the transcriptional regulatory network governing red-leaf formation in E.sac-rosancta,establishing a critical theoretical foundation for molecular breeding in ornamental plants.
基金supported by the National Natural Science Foundation of China(No.32271673)5·5 Engineering Research&Innovation Team Project of Beijing Forestry University(No.BLRC2023B06).
文摘Rapid urbanization has contributed to global increases in air pollution derived from urban areas.Unlike natural forests,urban forests are exposed to higher concentrations of airborne pollutants due to the strong urban-suburban-rural pollutant emission gradients.However,there remains a pressing lack of available information pertaining to the urban air pollutionrelated effects on the leaf economics spectrum,anatomical,ultrastructural,and stomatal traits of tree species along an urban-rural gradient.Here,the degree to which urban air pollution impacts the adaption of greening tree species and associated service functions was assessed by sampling five common tree species(Acer pictum,Fraxinus chinensis,Koelreuteria paniculata,Salix babylonica,Sophora japonica)along urban-rural-natural forests in the Beijing metropolitan region of China.These analyses revealed a significant reduction in leaf mass per unit area(-13.4%),leaf thickness(-16.7%),and stomatal area(-27.5%)with increasing proximity to areas of greater air pollution that coincide with significant increases in leaf tissue density(+12.6%),leaf nitrogen content(+10.1%),relative chlorophyll content(+2.7%),and stomatal density(+11.9%).Higher air pollution levels were associated with organelle changes including gradual disintegration of chloroplasts,larger intercellular spaces and apparent starch and plastoglobuli deposition.Air pollutionwas conducive to the strengthening of the trade-off potential and adaptation strategies of trees in urban ecosystems,which are associated with trees with a rapid investment return strategy associated with thick leaves and strong photosynthetic capacity.These results provide strong empirical evidence of the profound air pollution-induced changes in leaf functional traits and adaption ability of urban forest tree species.
文摘Sunflower leaf photosynthesis strongly depends on the leaf position in the plant stem conditioning,which directly affects other physiological processes.Therefore,a study of the leaf’s physiological status regarding the leaf position in the stem was performed on sunflowers in the flowering stage.Eight differently positioned leaves were investigated,starting with the youngest leaf on the top of the stem to the leaves of the stem bottom,assigned as the oldest senescent leaves.According to chlorophyll fluorescence(ChlF)parameters connected to photosystem II(PSII)processes,significant changes in PSII functioning occurred only in the senescent leaves,while photosystem I(PSI)describing parameters showed a linear decrease with leaf age,i.e.,position on the stem.The antioxidative status of the leaves was dynamic,as stress indicators(lipid peroxidation and hydrogen peroxide content)fluctuated regarding leaf position on the stem,but no link was found between the activities of antioxidative enzymes and oxidative stress indicators.Linear decrease trend of secondary metabolites(mainly phenolic compounds)correlated with antioxidant activity,except for some phenolic acids(caffeic and ferulic acid),which increased in senescent leaves.The most changes in the physiological status of the leaves were confirmed in senescent leaves,which stand out the importance of younger leaves in maintaining the plant’s vitality after flowering,which is the most important for sunflower yield.
