Triosephosphate isomerase(TPI)is an enzyme that functions in plant energy production,accumulation,and conversion.To understand its function in maize,we characterized a maize TPI mutant,zmtpi4.In comparison to the wild...Triosephosphate isomerase(TPI)is an enzyme that functions in plant energy production,accumulation,and conversion.To understand its function in maize,we characterized a maize TPI mutant,zmtpi4.In comparison to the wild type,zmtpi4 mutants showed altered ear development,reduced kernel weight and starch content,modified starch granule morphology,and altered amylose and amylopectin content.Protein,ATP,and pyruvate contents were reduced,indicating ZmTPI4 was involved in glycolysis.Although subcellular localization confirmed ZmTPI4 as a cytosolic rather than a plastid isoform of TPI,the zmtpi4 mutant showed reduced leaf size and chlorophyll content.Overexpression of ZmTPI4 in Arabidopsis led to enlarged leaves and increased seed weight,suggesting a positive regulatory role of ZmTPI4 in kernel weight and starch content.We conclude that ZmTPI4 functions in maize kernel development,starch synthesis,glycolysis,and photosynthesis.展开更多
The adulteration concentration of palm kernel oil(PKO)in virgin coconut oil(VCO)was quantified using near-infrared(NIR)hyperspectral imaging.Nowadays,some VCO is adulterated with lower-priced PKO to reduce production ...The adulteration concentration of palm kernel oil(PKO)in virgin coconut oil(VCO)was quantified using near-infrared(NIR)hyperspectral imaging.Nowadays,some VCO is adulterated with lower-priced PKO to reduce production costs,which diminishes the quality of the VCO.This study used NIR hyperspectral imaging in the wavelength region 900-1,650 nm to create a quantitative model for the detection of PKO contaminants(0-100%)in VCO and to develop predictive mapping.The prediction equation for the adulteration of VCO with PKO was constructed using the partial least squares regression method.The best predictive model was pre-processed using the standard normal variate method,and the coefficient of determination of prediction was 0.991,the root mean square error of prediction was 2.93%,and the residual prediction deviation was 10.37.The results showed that this model could be applied for quantifying the adulteration concentration of PKO in VCO.The prediction adulteration concentration mapping of VCO with PKO was created from a calibration model that showed the color level according to the adulteration concentration in the range of 0-100%.NIR hyperspectral imaging could be clearly used to quantify the adulteration of VCO with a color level map that provides a quick,accurate,and non-destructive detection method.展开更多
The extended kernel ridge regression(EKRR)method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models.These are:(i)the isospin-dependent A^(...The extended kernel ridge regression(EKRR)method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models.These are:(i)the isospin-dependent A^(1∕3) formula,(ii)relativistic continuum Hartree-Bogoliubov(RCHB)theory,(iii)Hartree-Fock-Bogoliubov(HFB)model HFB25,(iv)the Weizsacker-Skyrme(WS)model WS*,and(v)HFB25*model.In the last two models,the charge radii were calculated using a five-parameter formula with the nuclear shell corrections and deformations obtained from the WS and HFB25 models,respectively.For each model,the resultant root-mean-square deviation for the 1014 nuclei with proton number Z≥8 can be significantly reduced to 0.009-0.013 fm after considering the modification with the EKRR method.The best among them was the RCHB model,with a root-mean-square deviation of 0.0092 fm.The extrapolation abilities of the KRR and EKRR methods for the neutron-rich region were examined,and it was found that after considering the odd-even effects,the extrapolation power was improved compared with that of the original KRR method.The strong odd-even staggering of nuclear charge radii of Ca and Cu isotopes and the abrupt kinks across the neutron N=126 and 82 shell closures were also calculated and could be reproduced quite well by calculations using the EKRR method.展开更多
Adjusting agronomic measures to alleviate the kernel position effect in maize is important for ensuring high yields.In order to clarify whether the combined application of organic fertilizer and chemical fertilizer(CA...Adjusting agronomic measures to alleviate the kernel position effect in maize is important for ensuring high yields.In order to clarify whether the combined application of organic fertilizer and chemical fertilizer(CAOFCF)can alleviate the kernel position effect of summer maize,field experiments were conducted during the 2019 and 2020 growing seasons,and five treatments were assessed:CF,100%chemical fertilizer;OFCF1,15%organic fertilizer+85%chemical fertilizer;OFCF2,30%organic fertilizer+70%chemical fertilizer;OFCF3,45%organic fertilizer+55%chemical fertilizer;and OFCF4,60%organic fertilizer+40%chemical fertilizer.Compared with the CF treatment,the OFCF1 and OFCF2 treatments significantly alleviated the kernel position effect by increasing the weight ratio of inferior kernels to superior kernels and reducing the weight gap between the superior and inferior kernels.These effects were largely due to the improved filling and starch accumulation of inferior kernels.However,there were no obvious differences in the kernel position effect among plants treated with CF,OFCF3,or OFCF4 in most cases.Leaf area indexes,post-silking photosynthetic rates,and net assimilation rates were higher in plants treated with OFCF1 or OFCF2 than in those treated with CF,reflecting an enhanced photosynthetic capacity and improved postsilking dry matter accumulation(DMA)in the plants treated with OFCF1 or OFCF2.Compared with the CF treatment,the OFCF1 and OFCF2 treatments increased post-silking N uptake by 66.3 and 75.5%,respectively,which was the major factor driving post-silking photosynthetic capacity and DMA.Moreover,the increases in root DMA and zeatin riboside content observed following the OFCF1 and OFCF2 treatments resulted in reduced root senescence,which is associated with an increased post-silking N uptake.Analyses showed that post-silking N uptake,DMA,and grain yield in summer maize were negatively correlated with the kernel position effect.In conclusion,the combined application of 15-30%organic fertilizer and 70-85%chemical fertilizer alleviated the kernel position effect in summer maize by improving post-silking N uptake and DMA.These results provide new insights into how CAOFCF can be used to improve maize productivity.展开更多
Although it is recognized that the post-harvest system is most responsible for the loss of soybean quality,the real impact of this loss is still unknown.Brazilian regulation allows 15%and 30%of broken soybean for grou...Although it is recognized that the post-harvest system is most responsible for the loss of soybean quality,the real impact of this loss is still unknown.Brazilian regulation allows 15%and 30%of broken soybean for group I and group II(quality groups),respectively.However,the industry is not informed about the loss in the quality parameters of soybeans and its impacts during long-term storage.Therefore,the objective was to evaluate the effect of the breakage kernel percentage of soybean stored for 12 months.Content of 15% of breakage kernels did not affect soybean quality.However,content of 30% of breakage kernels affected significantly soybean quality,which was evidenced by the increase of up to 75% in moldy soybeans,72% in acidity,50% in leached solids,27% in electrical conductivity,and the decrease of up to 12% in soluble protein,6.4% in germination and 3.5% in thousand kernel weight after 8 months of storage.Although the legislation establishes a percentage limit,it is recommended to store soybeans with up to 15% breakage kernels.On the contrary,values higher than that can cause a significant reduction in soybean quality,resulting in commercial losses.展开更多
The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed wo...The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.展开更多
Metal trace elements (MTE) are among the most harmful micropollutants of natural waters. Eliminating them helps improve the quality and safety of drinking water and protect human health. In this work, we used mango ke...Metal trace elements (MTE) are among the most harmful micropollutants of natural waters. Eliminating them helps improve the quality and safety of drinking water and protect human health. In this work, we used mango kernel powder (MKP) as bioadsorbent material for removal of Cr (VI) from water. Uv-visible spectroscopy was used to monitor and quantify Cr (VI) during processing using the Beer-Lambert formula. Some parameters such as pH, mango powder, mass and contact time were optimized to determine adsorption capacity and chromium removal rate. Adsorption kinetics, equilibrium, isotherms and thermodynamic parameters such as ΔG˚, ΔH˚, and ΔS˚, as well as FTIR were studied to better understand the Cr (VI) removal process by MKP. The adsorption capacity reached 94.87 mg/g, for an optimal contact time of 30 min at 298 K. The obtained results are in accordance with a pseudo-second order Freundlich adsorption isotherm model. Finally FTIR was used to monitor the evolution of absorption bands, while Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) were used to evaluate surface properties and morphology of the adsorbent.展开更多
We provide a kernel-regularized method to give theory solutions for Neumann boundary value problem on the unit ball. We define the reproducing kernel Hilbert space with the spherical harmonics associated with an inner...We provide a kernel-regularized method to give theory solutions for Neumann boundary value problem on the unit ball. We define the reproducing kernel Hilbert space with the spherical harmonics associated with an inner product defined on both the unit ball and the unit sphere, construct the kernel-regularized learning algorithm from the view of semi-supervised learning and bound the upper bounds for the learning rates. The theory analysis shows that the learning algorithm has better uniform convergence according to the number of samples. The research can be regarded as an application of kernel-regularized semi-supervised learning.展开更多
The present article covers a simple approach to detect and subsequently identify in vivo metabolites of brodimoprim, using high performance liquid chromatography coupled to ion trap mass spectrometer(LC/ESI-MS), whi...The present article covers a simple approach to detect and subsequently identify in vivo metabolites of brodimoprim, using high performance liquid chromatography coupled to ion trap mass spectrometer(LC/ESI-MS), which is based on a data-dependent acquisition of isotope ions and result verified by full scan mass spectrum. The distinguished advantage of data-dependent scan is rapidness because it requires minimum sample preparation, and all the necessary data can be obtained in one chromatographic run. In addition, it is highly sensitive and selective, allowing detection of trace metabolites even in the presence of complex biomatrix. As a result, four phase-Ⅰ(M1--M4) and four Phase-Ⅱ(M5--M8) metabolites of brodimoprim were identified in urine after the oral administration of hrodimoprim to Wistar rats. Their chemical structures were proposed based on the interpretation of their CID fragmentation characterizations and the metabolic pathway was exhibited in this article.展开更多
Maize kernel moisture content(KMC)at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate(KDR)before and after physiological maturity.KMC and KDR are compl...Maize kernel moisture content(KMC)at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate(KDR)before and after physiological maturity.KMC and KDR are complex traits governed by multiple quantitative trait loci(QTL).Their genetic architecture is incompletely understood.We used a multiomics integration approach with an association panel to identify genes influencing KMC and KDR.A genome-wide association study using time-series KMC data from 7 to 70 days after pollination and their transformed KDR data revealed respectively 98and 279 loci significantly associated with KMC and KDR.Time-series transcriptome and proteome datasets were generated to construct KMC correlation networks,from which respectively 3111 and 759 module genes and proteins were identified as highly associated with KMC.Integrating multiomics analysis,several promising candidate genes for KMC and KDR,including Zm00001d047799 and Zm00001d035920,were identified.Further mutant experiments showed that Zm00001d047799,a gene encoding heat shock 70 kDa protein 5,reduced KMC in the late stage of kernel development.Our study provides resources for the identification of candidate genes influencing maize KMC and KDR,shedding light on the genetic architecture of dynamic changes in maize KMC.展开更多
How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue...How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.展开更多
This paper is aimed at solving the nonlinear time-fractional partial differential equation with two small parameters arising from option pricing model in financial economics.The traditional reproducing kernel(RK)metho...This paper is aimed at solving the nonlinear time-fractional partial differential equation with two small parameters arising from option pricing model in financial economics.The traditional reproducing kernel(RK)method which deals with this problem is very troublesome.This paper proposes a new method by adaptive multi-step piecewise interpolation reproducing kernel(AMPIRK)method for the first time.This method has three obvious advantages which are as follows.Firstly,the piecewise number is reduced.Secondly,the calculation accuracy is improved.Finally,the waste time caused by too many fragments is avoided.Then four numerical examples show that this new method has a higher precision and it is a more timesaving numerical method than the others.The research in this paper provides a powerful mathematical tool for solving time-fractional option pricing model which will play an important role in financial economics.展开更多
Compared with channel estimation method based on explicit training sequences,bandwidth is saved for those methods using superimposed training sequences,while it is wasted when Cyclic Prefix(CP) is added.In previous wo...Compared with channel estimation method based on explicit training sequences,bandwidth is saved for those methods using superimposed training sequences,while it is wasted when Cyclic Prefix(CP) is added.In previous work of McLernon,the Mean Square Error(MSE) performance of Data-Dependent Superimposed Training(DDST) without CP for Single-Input Single-Output(SISO) system was analyzed under the assumption that the data-dependent sequence matrix was a circulant matrix and not interfered by others.In fact,for the system without CP,the data-dependent sequence matrix is not circulant any more and will be interfered.This paper derives the exact expression of MSE for the system without CP and also gives its extension to Multiple-Input Multiple-Output(MIMO) system without CP.展开更多
Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accura...Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.展开更多
In Côte d’Ivoire, coconut and palm cultivation are very developed. Industrial utilization of these crops fruits is generated some under products that can be used in breeding. The aim of this study was to test...In Côte d’Ivoire, coconut and palm cultivation are very developed. Industrial utilization of these crops fruits is generated some under products that can be used in breeding. The aim of this study was to test the potentialities in the breeding of cattle-cake of coconut and cattle-cake of palm kernel when they are incorporated in diets of piglet fattening. 18 piglets teamed up into three homogenous groups (three males castrate and three females) were used. The control group was fed a diet made with 55% of maize + 10% of bran maize + 25% fish powder + 4% of ash + 3% of salt. The test 1 group called diet ESSAI 1 was fed a mixture of 70% of the control diet and 30% of cattle-cake of coconut. The test 2 group called diet ESSAI 2 was fed with a mixture of 70% of the control diet and 30% of cattle-cake of palm kernel. The analysis made showed that the two cattle-cake are nutritiously rich and their incorporation in diets at degree of 30% of the total diet induced growth performance similar to that obtained with the control diet. Animals of each group were in good health and diets EASSAI 1 and ESSAI 2 were cheaper than the control diet. Then, cattle-cake of coconut and cattle-cake of palm kernel can be recommended as part of the diet of piglet (about 30%) in order to enhance profit with the same growth performance.展开更多
Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly bein...Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being challenged.To address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone data.By default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of themodel.To improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV data.Based on the above improvements,we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)dataset.The experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.展开更多
Background:Genomic selection(GS)has revolutionized animal and plant breeding after the first implementation via early selection before measuring phenotypes.Besides genome,transcriptome and metabolome information are i...Background:Genomic selection(GS)has revolutionized animal and plant breeding after the first implementation via early selection before measuring phenotypes.Besides genome,transcriptome and metabolome information are increasingly considered new sources for GS.Difficulties in building the model with multi-omics data for GS and the limit of specimen availability have both delayed the progress of investigating multi-omics.Results:We utilized the Cosine kernel to map genomic and transcriptomic data as n×n symmetric matrix(G matrix and T matrix),combined with the best linear unbiased prediction(BLUP)for GS.Here,we defined five kernel-based prediction models:genomic BLUP(GBLUP),transcriptome-BLUP(TBLUP),multi-omics BLUP(MBLUP,M=ratio×G+(1-ratio)×T),multi-omics single-step BLUP(mss BLUP),and weighted multi-omics single-step BLUP(wmss BLUP)to integrate transcribed individuals and genotyped resource population.The predictive accuracy evaluations in four traits of the Chinese Simmental beef cattle population showed that(1)MBLUP was far preferred to GBLUP(ratio=1.0),(2)the prediction accuracy of wmss BLUP and mss BLUP had 4.18%and 3.37%average improvement over GBLUP,(3)We also found the accuracy of wmss BLUP increased with the growing proportion of transcribed cattle in the whole resource population.Conclusions:We concluded that the inclusion of transcriptome data in GS had the potential to improve accuracy.Moreover,wmss BLUP is accepted to be a promising alternative for the present situation in which plenty of individuals are genotyped when fewer are transcribed.展开更多
Chromatin accessibility plays a vital role in gene transcriptional regulation.However,the regulatory mechanism of chromatin accessibility,as well as its role in regulating crucial gene expression and kernel developmen...Chromatin accessibility plays a vital role in gene transcriptional regulation.However,the regulatory mechanism of chromatin accessibility,as well as its role in regulating crucial gene expression and kernel development in maize(Zea mays)are poorly understood.In this study,we isolated a maize kernel mutant designated as defective kernel219(dek219),which displays opaque endosperm and embryo abortion.Dek219 encodes the DICER-LIKE1(DCL1)protein,an essential enzyme in mi RNA biogenesis.Loss of function of Dek219 results in significant reductions in the expression levels of most mi RNAs and histone genes.Further research showed that the Heat shock transcription factor17(Hsf17)-Zm00001d016571 module may be one of the factors affecting the expression of histone genes.Assay results for transposase-accessible chromatin sequencing(ATAC-seq)indicated that the chromatin accessibility of dek219 is altered compared with that of wild type(WT),which may regulate the expression of crucial genes in kernel development.By analyzing differentially expressed genes(DEGs)and differentially accessible chromatin regions(ACRs)between WT and dek219,we identified 119 candidate genes that are regulated by chromatin accessibility,including some reported to be crucial genes for kernel development.Taken together,these results suggest that Dek219 affects chromatin accessibility and the expression of crucial genes that are required for maize kernel development.展开更多
In this paper, we extend the state-space kriging(SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions a...In this paper, we extend the state-space kriging(SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions as linear combinations of past outputs. To model the nonlinear dynamics of the system, we propose the kernel-based state-space kriging(K-SSK), a new version of the SSK where kernel functions are used instead of resorting to considerations about the locality of the data. Also, a Kalman filter can be used to improve the predictions at each time step in the case of noisy measurements. A constrained tracking nonlinear model predictive control(NMPC) scheme using the black-box input-output model obtained by means of the K-SSK prediction method is proposed. Finally, a simulation example and a real experiment are provided in order to assess the performance of the proposed controller.展开更多
Torreya grandis is a characteristic rare economic tree species in subtropical mountainous areas. The nut has a variety of bioactive compounds, such as polyphenols and vitamins. However, the carotenoids compositions an...Torreya grandis is a characteristic rare economic tree species in subtropical mountainous areas. The nut has a variety of bioactive compounds, such as polyphenols and vitamins. However, the carotenoids compositions and its regulatory mechanisms underlying carotenoid biosynthesis in kernels remain unknown. Here, the major carotenoids in the kernel were analyzed. The result showed that lutein and β-carotene were the predominant carotenoids in the kernel, while low levels of a-carotenoid and zeaxanthin were detected. Lutein and β-carotene were decreased during the maturation of kernel. Expression analysis by RNA-seq and q RT-PCR indicated that the expressions of TgCYP97A3 and TgLCYB were also reduced during the maturation of kernel. The contents of lutein or β-carotene were obviously increased in tobacco transiently overexpressing TgCYP97A3 or TgLCYB. Moreover, DPPH radical scavenging activity and FRAP were also significantly enhanced. In addition, several MYB and WRKY transcription factors TgMYBS3, TgMYB48 and TgWRKY11 were identified to positively regulate the TgCYP97A3 expression, while TgMYB48, TgWRKY2 and TgWRKY11 could upregulate the TgLCYB expression. The illustration of carotenoids biosynthesis and its molecular mechanism in kernels not only provides a basis for understanding carotenoids biosynthesis in kernels, but also enables the use of molecular biotechnology to develop new health products rich in carotenoids based on T. grandis nuts.展开更多
基金supported by the Major Public Welfare Projects of Henan Province(201300111100 to Yuling Li)Zhongyuan Scholars in Henan Province(22400510003 to Yuling Li)+2 种基金Tackle Program of Agricultural Seed in Henan Province(2022010201 to Yuling Li)Technical System of Maize Industry in Henan Province(HARS-2202-S to Yuling Li)State Key Laboratory of Wheat and Maize Crop Science(SKL2023ZZ05)。
文摘Triosephosphate isomerase(TPI)is an enzyme that functions in plant energy production,accumulation,and conversion.To understand its function in maize,we characterized a maize TPI mutant,zmtpi4.In comparison to the wild type,zmtpi4 mutants showed altered ear development,reduced kernel weight and starch content,modified starch granule morphology,and altered amylose and amylopectin content.Protein,ATP,and pyruvate contents were reduced,indicating ZmTPI4 was involved in glycolysis.Although subcellular localization confirmed ZmTPI4 as a cytosolic rather than a plastid isoform of TPI,the zmtpi4 mutant showed reduced leaf size and chlorophyll content.Overexpression of ZmTPI4 in Arabidopsis led to enlarged leaves and increased seed weight,suggesting a positive regulatory role of ZmTPI4 in kernel weight and starch content.We conclude that ZmTPI4 functions in maize kernel development,starch synthesis,glycolysis,and photosynthesis.
基金supported by the Thailand Research Fund through the Royal Golden Jubilee Ph.D.Program(PHD/0225/2561)the Faculty of Engineering,Kamphaeng Saen Campus,Kasetsart University,Thailand。
文摘The adulteration concentration of palm kernel oil(PKO)in virgin coconut oil(VCO)was quantified using near-infrared(NIR)hyperspectral imaging.Nowadays,some VCO is adulterated with lower-priced PKO to reduce production costs,which diminishes the quality of the VCO.This study used NIR hyperspectral imaging in the wavelength region 900-1,650 nm to create a quantitative model for the detection of PKO contaminants(0-100%)in VCO and to develop predictive mapping.The prediction equation for the adulteration of VCO with PKO was constructed using the partial least squares regression method.The best predictive model was pre-processed using the standard normal variate method,and the coefficient of determination of prediction was 0.991,the root mean square error of prediction was 2.93%,and the residual prediction deviation was 10.37.The results showed that this model could be applied for quantifying the adulteration concentration of PKO in VCO.The prediction adulteration concentration mapping of VCO with PKO was created from a calibration model that showed the color level according to the adulteration concentration in the range of 0-100%.NIR hyperspectral imaging could be clearly used to quantify the adulteration of VCO with a color level map that provides a quick,accurate,and non-destructive detection method.
基金This work was supported by the National Natural Science Foundation of China(Nos.11875027,11975096).
文摘The extended kernel ridge regression(EKRR)method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models.These are:(i)the isospin-dependent A^(1∕3) formula,(ii)relativistic continuum Hartree-Bogoliubov(RCHB)theory,(iii)Hartree-Fock-Bogoliubov(HFB)model HFB25,(iv)the Weizsacker-Skyrme(WS)model WS*,and(v)HFB25*model.In the last two models,the charge radii were calculated using a five-parameter formula with the nuclear shell corrections and deformations obtained from the WS and HFB25 models,respectively.For each model,the resultant root-mean-square deviation for the 1014 nuclei with proton number Z≥8 can be significantly reduced to 0.009-0.013 fm after considering the modification with the EKRR method.The best among them was the RCHB model,with a root-mean-square deviation of 0.0092 fm.The extrapolation abilities of the KRR and EKRR methods for the neutron-rich region were examined,and it was found that after considering the odd-even effects,the extrapolation power was improved compared with that of the original KRR method.The strong odd-even staggering of nuclear charge radii of Ca and Cu isotopes and the abrupt kinks across the neutron N=126 and 82 shell closures were also calculated and could be reproduced quite well by calculations using the EKRR method.
基金financially supported by the HAAFS Science and Technology Innovation Special Project China(2022KJCXZX-LYS-9)the Natural Science Foundation of Hebei Province China(C2021301004)the Key Research and Dvelopment Program of Hebei Province China(20326401D)。
文摘Adjusting agronomic measures to alleviate the kernel position effect in maize is important for ensuring high yields.In order to clarify whether the combined application of organic fertilizer and chemical fertilizer(CAOFCF)can alleviate the kernel position effect of summer maize,field experiments were conducted during the 2019 and 2020 growing seasons,and five treatments were assessed:CF,100%chemical fertilizer;OFCF1,15%organic fertilizer+85%chemical fertilizer;OFCF2,30%organic fertilizer+70%chemical fertilizer;OFCF3,45%organic fertilizer+55%chemical fertilizer;and OFCF4,60%organic fertilizer+40%chemical fertilizer.Compared with the CF treatment,the OFCF1 and OFCF2 treatments significantly alleviated the kernel position effect by increasing the weight ratio of inferior kernels to superior kernels and reducing the weight gap between the superior and inferior kernels.These effects were largely due to the improved filling and starch accumulation of inferior kernels.However,there were no obvious differences in the kernel position effect among plants treated with CF,OFCF3,or OFCF4 in most cases.Leaf area indexes,post-silking photosynthetic rates,and net assimilation rates were higher in plants treated with OFCF1 or OFCF2 than in those treated with CF,reflecting an enhanced photosynthetic capacity and improved postsilking dry matter accumulation(DMA)in the plants treated with OFCF1 or OFCF2.Compared with the CF treatment,the OFCF1 and OFCF2 treatments increased post-silking N uptake by 66.3 and 75.5%,respectively,which was the major factor driving post-silking photosynthetic capacity and DMA.Moreover,the increases in root DMA and zeatin riboside content observed following the OFCF1 and OFCF2 treatments resulted in reduced root senescence,which is associated with an increased post-silking N uptake.Analyses showed that post-silking N uptake,DMA,and grain yield in summer maize were negatively correlated with the kernel position effect.In conclusion,the combined application of 15-30%organic fertilizer and 70-85%chemical fertilizer alleviated the kernel position effect in summer maize by improving post-silking N uptake and DMA.These results provide new insights into how CAOFCF can be used to improve maize productivity.
基金Coordenacao de Aperfeicoamento de Pessoal de Nível Superior - Brasil (CAPES)Fundacao de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS)+2 种基金Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)financed in part by Coordenacao de Aperfeicoamento de Pessoal de Nível Superior-Brasil(CAPES)-Finance code 001,Fundacao de Amparoa Pesquisa do Estado do Rio Grande do Sul(FAPERGS)-Finances code 17/2551-0000935-5,22/2551-0001051-2,21/2551-0002255-8Conselho Nacional de Desenvolvimento Científico e Tecnologico(CNPq)-Finance codes 205518/2018-4,312603/2018-5.
文摘Although it is recognized that the post-harvest system is most responsible for the loss of soybean quality,the real impact of this loss is still unknown.Brazilian regulation allows 15%and 30%of broken soybean for group I and group II(quality groups),respectively.However,the industry is not informed about the loss in the quality parameters of soybeans and its impacts during long-term storage.Therefore,the objective was to evaluate the effect of the breakage kernel percentage of soybean stored for 12 months.Content of 15% of breakage kernels did not affect soybean quality.However,content of 30% of breakage kernels affected significantly soybean quality,which was evidenced by the increase of up to 75% in moldy soybeans,72% in acidity,50% in leached solids,27% in electrical conductivity,and the decrease of up to 12% in soluble protein,6.4% in germination and 3.5% in thousand kernel weight after 8 months of storage.Although the legislation establishes a percentage limit,it is recommended to store soybeans with up to 15% breakage kernels.On the contrary,values higher than that can cause a significant reduction in soybean quality,resulting in commercial losses.
文摘The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.
文摘Metal trace elements (MTE) are among the most harmful micropollutants of natural waters. Eliminating them helps improve the quality and safety of drinking water and protect human health. In this work, we used mango kernel powder (MKP) as bioadsorbent material for removal of Cr (VI) from water. Uv-visible spectroscopy was used to monitor and quantify Cr (VI) during processing using the Beer-Lambert formula. Some parameters such as pH, mango powder, mass and contact time were optimized to determine adsorption capacity and chromium removal rate. Adsorption kinetics, equilibrium, isotherms and thermodynamic parameters such as ΔG˚, ΔH˚, and ΔS˚, as well as FTIR were studied to better understand the Cr (VI) removal process by MKP. The adsorption capacity reached 94.87 mg/g, for an optimal contact time of 30 min at 298 K. The obtained results are in accordance with a pseudo-second order Freundlich adsorption isotherm model. Finally FTIR was used to monitor the evolution of absorption bands, while Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) were used to evaluate surface properties and morphology of the adsorbent.
文摘We provide a kernel-regularized method to give theory solutions for Neumann boundary value problem on the unit ball. We define the reproducing kernel Hilbert space with the spherical harmonics associated with an inner product defined on both the unit ball and the unit sphere, construct the kernel-regularized learning algorithm from the view of semi-supervised learning and bound the upper bounds for the learning rates. The theory analysis shows that the learning algorithm has better uniform convergence according to the number of samples. The research can be regarded as an application of kernel-regularized semi-supervised learning.
基金the National Natural Science Foundation of China(Nos.30630075 and 20675056)Natural Science Founda-tion of Tianjin City,China(No.07JCYBJC01600)
文摘The present article covers a simple approach to detect and subsequently identify in vivo metabolites of brodimoprim, using high performance liquid chromatography coupled to ion trap mass spectrometer(LC/ESI-MS), which is based on a data-dependent acquisition of isotope ions and result verified by full scan mass spectrum. The distinguished advantage of data-dependent scan is rapidness because it requires minimum sample preparation, and all the necessary data can be obtained in one chromatographic run. In addition, it is highly sensitive and selective, allowing detection of trace metabolites even in the presence of complex biomatrix. As a result, four phase-Ⅰ(M1--M4) and four Phase-Ⅱ(M5--M8) metabolites of brodimoprim were identified in urine after the oral administration of hrodimoprim to Wistar rats. Their chemical structures were proposed based on the interpretation of their CID fragmentation characterizations and the metabolic pathway was exhibited in this article.
基金supported by Natural Science Foundation of Shaanxi Province(S2021-JC-WT-006)the National Key Research and Development Program of China(2018YFD0100200)+1 种基金the China Postdoctoral Science Foundation(2018M633588)the China Agriculture Research System(CARS-02-77)。
文摘Maize kernel moisture content(KMC)at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate(KDR)before and after physiological maturity.KMC and KDR are complex traits governed by multiple quantitative trait loci(QTL).Their genetic architecture is incompletely understood.We used a multiomics integration approach with an association panel to identify genes influencing KMC and KDR.A genome-wide association study using time-series KMC data from 7 to 70 days after pollination and their transformed KDR data revealed respectively 98and 279 loci significantly associated with KMC and KDR.Time-series transcriptome and proteome datasets were generated to construct KMC correlation networks,from which respectively 3111 and 759 module genes and proteins were identified as highly associated with KMC.Integrating multiomics analysis,several promising candidate genes for KMC and KDR,including Zm00001d047799 and Zm00001d035920,were identified.Further mutant experiments showed that Zm00001d047799,a gene encoding heat shock 70 kDa protein 5,reduced KMC in the late stage of kernel development.Our study provides resources for the identification of candidate genes influencing maize KMC and KDR,shedding light on the genetic architecture of dynamic changes in maize KMC.
基金supported by the National Natural Science Foundation of China(Grant No.42002134)China Postdoctoral Science Foundation(Grant No.2021T140735)Science Foundation of China University of Petroleum,Beijing(Grant Nos.2462020XKJS02 and 2462020YXZZ004).
文摘How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.
基金the National Natural Science Foundation of China(Grant Nos.71961022,11902163,12265020,and 12262024)the Natural Science Foundation of Inner Mongolia Autonomous Region of China(Grant Nos.2019BS01011 and 2022MS01003)+5 种基金2022 Inner Mongolia Autonomous Region Grassland Talents Project-Young Innovative and Entrepreneurial Talents(Mingjing Du)2022 Talent Development Foundation of Inner Mongolia Autonomous Region of China(Ming-Jing Du)the Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region Program(Grant No.NJYT-20-B18)the Key Project of High-quality Economic Development Research Base of Yellow River Basin in 2022(Grant No.21HZD03)2022 Inner Mongolia Autonomous Region International Science and Technology Cooperation High-end Foreign Experts Introduction Project(Ge Kai)MOE(Ministry of Education in China)Humanities and Social Sciences Foundation(Grants No.20YJC860005).
文摘This paper is aimed at solving the nonlinear time-fractional partial differential equation with two small parameters arising from option pricing model in financial economics.The traditional reproducing kernel(RK)method which deals with this problem is very troublesome.This paper proposes a new method by adaptive multi-step piecewise interpolation reproducing kernel(AMPIRK)method for the first time.This method has three obvious advantages which are as follows.Firstly,the piecewise number is reduced.Secondly,the calculation accuracy is improved.Finally,the waste time caused by too many fragments is avoided.Then four numerical examples show that this new method has a higher precision and it is a more timesaving numerical method than the others.The research in this paper provides a powerful mathematical tool for solving time-fractional option pricing model which will play an important role in financial economics.
基金Supported by the National Natural Science Foundation of China (No.60772087,No.50803016,No.60975004,No.60902023)the Foundation for the Author of National Excellent Doctoral Dissertation of P.R. China (No.200341)+1 种基金the National 863 High-Tech R&D Program (No.2007AA01Z 228)the open research fund of Key Laboratory of Information Coding and Transmission,Southwest Jiaotong University
文摘Compared with channel estimation method based on explicit training sequences,bandwidth is saved for those methods using superimposed training sequences,while it is wasted when Cyclic Prefix(CP) is added.In previous work of McLernon,the Mean Square Error(MSE) performance of Data-Dependent Superimposed Training(DDST) without CP for Single-Input Single-Output(SISO) system was analyzed under the assumption that the data-dependent sequence matrix was a circulant matrix and not interfered by others.In fact,for the system without CP,the data-dependent sequence matrix is not circulant any more and will be interfered.This paper derives the exact expression of MSE for the system without CP and also gives its extension to Multiple-Input Multiple-Output(MIMO) system without CP.
基金support of national natural science foundation of China(No.52067021)natural science foundation of Xinjiang(2022D01C35)+1 种基金excellent youth scientific and technological talents plan of Xinjiang(No.2019Q012)major science and technology special project of Xinjiang Uygur Autonomous Region(2022A01002-2).
文摘Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.
文摘In Côte d’Ivoire, coconut and palm cultivation are very developed. Industrial utilization of these crops fruits is generated some under products that can be used in breeding. The aim of this study was to test the potentialities in the breeding of cattle-cake of coconut and cattle-cake of palm kernel when they are incorporated in diets of piglet fattening. 18 piglets teamed up into three homogenous groups (three males castrate and three females) were used. The control group was fed a diet made with 55% of maize + 10% of bran maize + 25% fish powder + 4% of ash + 3% of salt. The test 1 group called diet ESSAI 1 was fed a mixture of 70% of the control diet and 30% of cattle-cake of coconut. The test 2 group called diet ESSAI 2 was fed with a mixture of 70% of the control diet and 30% of cattle-cake of palm kernel. The analysis made showed that the two cattle-cake are nutritiously rich and their incorporation in diets at degree of 30% of the total diet induced growth performance similar to that obtained with the control diet. Animals of each group were in good health and diets EASSAI 1 and ESSAI 2 were cheaper than the control diet. Then, cattle-cake of coconut and cattle-cake of palm kernel can be recommended as part of the diet of piglet (about 30%) in order to enhance profit with the same growth performance.
基金supported by the Natural Science Foundation of The Jiangsu Higher Education Institutions of China(Grant No.19JKB520031).
文摘Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being challenged.To address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone data.By default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of themodel.To improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV data.Based on the above improvements,we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)dataset.The experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.
基金funds from the National Natural Science Foundations of China(32172693)the Program of National Beef Cattle and Yak Industrial Technology System(CARS-37)。
文摘Background:Genomic selection(GS)has revolutionized animal and plant breeding after the first implementation via early selection before measuring phenotypes.Besides genome,transcriptome and metabolome information are increasingly considered new sources for GS.Difficulties in building the model with multi-omics data for GS and the limit of specimen availability have both delayed the progress of investigating multi-omics.Results:We utilized the Cosine kernel to map genomic and transcriptomic data as n×n symmetric matrix(G matrix and T matrix),combined with the best linear unbiased prediction(BLUP)for GS.Here,we defined five kernel-based prediction models:genomic BLUP(GBLUP),transcriptome-BLUP(TBLUP),multi-omics BLUP(MBLUP,M=ratio×G+(1-ratio)×T),multi-omics single-step BLUP(mss BLUP),and weighted multi-omics single-step BLUP(wmss BLUP)to integrate transcribed individuals and genotyped resource population.The predictive accuracy evaluations in four traits of the Chinese Simmental beef cattle population showed that(1)MBLUP was far preferred to GBLUP(ratio=1.0),(2)the prediction accuracy of wmss BLUP and mss BLUP had 4.18%and 3.37%average improvement over GBLUP,(3)We also found the accuracy of wmss BLUP increased with the growing proportion of transcribed cattle in the whole resource population.Conclusions:We concluded that the inclusion of transcriptome data in GS had the potential to improve accuracy.Moreover,wmss BLUP is accepted to be a promising alternative for the present situation in which plenty of individuals are genotyped when fewer are transcribed.
基金the National Natural Science Foundation of China(32072071)the National Key Research and Development Program of China(2021YFF1000304)。
文摘Chromatin accessibility plays a vital role in gene transcriptional regulation.However,the regulatory mechanism of chromatin accessibility,as well as its role in regulating crucial gene expression and kernel development in maize(Zea mays)are poorly understood.In this study,we isolated a maize kernel mutant designated as defective kernel219(dek219),which displays opaque endosperm and embryo abortion.Dek219 encodes the DICER-LIKE1(DCL1)protein,an essential enzyme in mi RNA biogenesis.Loss of function of Dek219 results in significant reductions in the expression levels of most mi RNAs and histone genes.Further research showed that the Heat shock transcription factor17(Hsf17)-Zm00001d016571 module may be one of the factors affecting the expression of histone genes.Assay results for transposase-accessible chromatin sequencing(ATAC-seq)indicated that the chromatin accessibility of dek219 is altered compared with that of wild type(WT),which may regulate the expression of crucial genes in kernel development.By analyzing differentially expressed genes(DEGs)and differentially accessible chromatin regions(ACRs)between WT and dek219,we identified 119 candidate genes that are regulated by chromatin accessibility,including some reported to be crucial genes for kernel development.Taken together,these results suggest that Dek219 affects chromatin accessibility and the expression of crucial genes that are required for maize kernel development.
基金supported by the Agencia Estatal de Investigación (AEI)-Spain (PID2019-106212RB-C41/AEI/10.13039/501100011033)Junta de Andalucía and FEDER funds (P20_00546)。
文摘In this paper, we extend the state-space kriging(SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions as linear combinations of past outputs. To model the nonlinear dynamics of the system, we propose the kernel-based state-space kriging(K-SSK), a new version of the SSK where kernel functions are used instead of resorting to considerations about the locality of the data. Also, a Kalman filter can be used to improve the predictions at each time step in the case of noisy measurements. A constrained tracking nonlinear model predictive control(NMPC) scheme using the black-box input-output model obtained by means of the K-SSK prediction method is proposed. Finally, a simulation example and a real experiment are provided in order to assess the performance of the proposed controller.
基金funded by the National Natural Science Foundation of China (Grant No.U20A2049)the Key Research and Development Program of Zhejiang Province (Grant No.2021C02001)the Scienti?c Research Startup Fund Project of Zhejiang A&F University (Grant No.2022LFR033)。
文摘Torreya grandis is a characteristic rare economic tree species in subtropical mountainous areas. The nut has a variety of bioactive compounds, such as polyphenols and vitamins. However, the carotenoids compositions and its regulatory mechanisms underlying carotenoid biosynthesis in kernels remain unknown. Here, the major carotenoids in the kernel were analyzed. The result showed that lutein and β-carotene were the predominant carotenoids in the kernel, while low levels of a-carotenoid and zeaxanthin were detected. Lutein and β-carotene were decreased during the maturation of kernel. Expression analysis by RNA-seq and q RT-PCR indicated that the expressions of TgCYP97A3 and TgLCYB were also reduced during the maturation of kernel. The contents of lutein or β-carotene were obviously increased in tobacco transiently overexpressing TgCYP97A3 or TgLCYB. Moreover, DPPH radical scavenging activity and FRAP were also significantly enhanced. In addition, several MYB and WRKY transcription factors TgMYBS3, TgMYB48 and TgWRKY11 were identified to positively regulate the TgCYP97A3 expression, while TgMYB48, TgWRKY2 and TgWRKY11 could upregulate the TgLCYB expression. The illustration of carotenoids biosynthesis and its molecular mechanism in kernels not only provides a basis for understanding carotenoids biosynthesis in kernels, but also enables the use of molecular biotechnology to develop new health products rich in carotenoids based on T. grandis nuts.