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Autophagy receptor ZmNBR1 promotes the autophagic degradation of ZmBRI1a and enhances drought tolerance in maize

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Journal of Integrative Plant Biology 2024年第6期66卷 1068-1086页

作者： Yang Xiang Guangdong li Qian li Yingxue Niu Yitian Pan Yuan Cheng Xiangli Bian Chongyang Zhao yuanhong Wang Aying Zhang College of life Sciences Nanjing Agricultural University State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization Nanjing Agricultural University Sanya Institute of Nanjing Agricultural University Nanjing Agricultural University

Drought stress is a crucial environmental factor that limits plant growth, development, and productivity.Autophagy of misfolded proteins can help alleviate the damage caused in plants experiencing drought.However, the mechanism of autophagy-mediated drought tolerance in plants remains largely unknown.Here, we cloned the gene for a maize(Zea mays)selective autophagy receptor, NEXT TO BRCA1GENE 1(ZmNBR1), and identified its role in the response to drought stress. We observed that drought stress increased the accumulation of autophagosomes. RNA sequencing and reverse transcriptionquantitative polymerase chain reaction showed that ZmNBR1 is markedly induced by drought stress. ZmNBR1 overexpression enhanced drought tolerance, while its knockdown reduced drought tolerance in maize. Our results established that ZmNBR1 mediates the increase in autophagosomes and autophagic activity under drought stress.ZmNBR1 also affects the expression of genes related to autophagy under drought stress. Moreover, we determined that BRASSINOSTEROID INSENSITIVE1A(ZmBRI1a), a brassinosteroid receptor of the BRI1-like family, interacts with ZmNBR1. Phenotype analysis showed that ZmBRI1a negatively regulates drought tolerance in maize, and genetic analysis indicated that ZmNBR1 acts upstream of ZmBRI1a in regulating drought tolerance. Furthermore, ZmNBR1facilitates the autophagic degradation of ZmBRI1a under drought stress. Taken together, our results reveal that ZmNBR1 regulates the expression of autophagy-related genes, thereby increasing autophagic activity and promoting the autophagic degradation of ZmBRI1a under drought stress, thus enhancing drought tolerance in maize. These findings provide new insights into the autophagy degradation of brassinosteroid signaling components by the autophagy receptor NBR1 under drought stress.

关键词： autophagy receptor drought tolerance Zea mays ZmBRI1a ZmNBR1

Suppression of ITPKB degradation by Trim25 confers TMZ resistance in glioblastoma through ROS homeostasis

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Signal Transduction and Targeted Therapy 2024年第4期9卷 1637-1650页

作者： Yuanliang Yan Shangjun Zhou Xi Chen Qiaoli Yi Songshan Feng Zijin Zhao yuanhong liu Qiuju liang Zhijie Xu Zhi li Lunquan Sun Department of Pharmacy Xiangya HospitalCentral South UniversityChangsha 410008China Department of Neurosurgery Xiangya HospitalCentral South UniversityChangsha 410008China Department of Pathology Xiangya HospitalCentral South UniversityChangsha 410008China Xiangya Cancer Center Xiangya HospitalCentral South UniversityChangsha 410008China Key Laboratory of Molecular Radiation Oncology Hunan Province Changsha 410008China institute of Cancer Research National Clinical Research Center for Geriatric Disorders(Xiangya)Xiangya HospitalCentral South UniversityChangsha 410008China

Temozolomide(TMZ)represents a standard-of-care chemotherapeutic agent in glioblastoma(GBM).However,the development of drug resistance constitutes a significant hurdle in the treatment of malignant glioma.Although specific innovative approaches,such as immunotherapy,have shown favorable clinical outcomes,the inherent invasiveness of most gliomas continues to make them challenging to treat.Consequently,there is an urgent need to identify effective therapeutic targets for gliomas to overcome chemoresistance and facilitate drug development.This investigation used mass spectrometry to examine the proteomic profiles of six pairs of GBM patients who underwent standard-of-care treatment and surgery for both primary and recurrent tumors.A total of 648 proteins exhibiting significant differential expression were identified.Gene Set Enrichment Analysis(GSEA)unveiled notable alterations in pathways related to METABOliSM_OF_liPIDS and BIOLOGICAL_OXIDATIONS between the primary and recurrent groups.Validation through glioma tissue arrays and the Xiangya cohort confirmed substantial upregulation of inositol 1,4,5-triphosphate(IP3)kinase B(ITPKB)in the recurrence group,correlating with poor survival in glioma patients.In TMZ-resistant cells,the depletion of ITPKB led to an increase in reactive oxygen species(ROS)related to NADPH oxidase(NOX)activity and restored cell sensitivity to TMz.Mechanistically,the decreased phosphorylation of the E3 ligase Trim25 at the S100 position in recurrent GBM samples accounted for the weakened ITPKB ubiquitination.This,in turn,elevated ITPKB stability and impaired ROS production.Furthermore,ITPKB depletion or the ITPKB inhibitor GNF362 effectively overcome TMZ chemoresistance in a glioma xenograft mouse model.These findings reveal a novel mechanism underlying TMZ resistance and propose ITPKB as a promising therapeutic target forTMZ-resistant GBM.

关键词： TMZ homeostasis impaired

Point Cloud Completion of Plant Leaves under Occlusion Conditions Based on Deep Learning

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Plant Phenomics 2023年第4期5卷 852-863页

作者： Haibo Chen Shengbo liu Congyue Wang Chaofeng Wang Kangye Gong yuanhong li Yubin Lan Experimental Basis and Practical Training Center South China Agricultural UniversityGuangzhouChina National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology GuangzhouChina College of Electronics Engineering(College of Artificial Intelligence) South China Agricultural UniversityGuangzhouChina College of Engineering South China Agricultural UniversityGuangzhouChina Department of Biological and Agricultural Engineering Texas A&M UniversityCollege StationTXUnited States

The utilization of 3-dimensional point cloud technology for non-invasive measurement of plant phenotypic parameters can furnish important data for plant breeding,agricultural production,and diverse research applications.Nevertheless,the utilization of depth sensors and other tools for capturing plant point clouds often results in missing and incomplete data due to the limitations of 2.5D imaging features and leaf occlusion.This drawback obstructed the accurate extraction of phenotypic parameters.Hence,this study presented a solution for incomplete flowering Chinese Cabbage point clouds using Point Fractal Network-based techniques.The study performed experiments on flowering Chinese Cabbage by constructing a point cloud dataset of their leaves and training the network.The findings demonstrated that our network is stable and robust,as it can effectively complete diverse leaf point cloud morphologies,missing ratios,and multi-missing scenarios.A novel framework is presented for 3D plant reconstruction using a single-view RGB-D(Red,Green,Blue and Depth)image.This method leveraged deep learning to complete localized incomplete leaf point clouds acquired by RGB-D cameras under occlusion conditions.Additionally,the extracted leaf area parameters,based on triangular mesh,were compared with the measured values.The outcomes revealed that prior to the point cloud completion,the R^(2)value of the flowering Chinese Cabbage's estimated leaf area(in comparison to the standard reference value)was 0.9162.The root mean square error(RMSE)was 15.88 cm^(2),and the average relative error was 22.11%.However,post-completion,the estimated value of leaf area witnessed a significant improvement,with an R^(2)of 0.9637,an RMSE of 6.79 cm^(2),and average relative error of 8.82%.The accuracy of estimating the phenotypic parameters has been enhanced significantly,enabling efficient retrieval of such parameters.This development offers a fresh perspective for non-destructive identification of plant phenotypes.

关键词： Deep incomplete Plant

Policy-Based Group Signature Scheme from Lattice

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Computers, Materials & Continua 2022年第8期72卷 4069-4085页

作者： Yongli Tang yuanhong li Qing Ye Ying li Xiaojun Wang School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China School of Electronic Engineering Dublin City UniversityDublin 9Ireland

Although the existing group signature schemes from lattice have been optimized for efficiency,the signing abilities of eachmember in the group are relatively single.It may not be suitable for complex applications.Inspired by the pioneering work of Bellare and Fuchsbauer,we present a primitive called policy-based group signature.In policy-based group signatures,group members can on behalf of the group to sign documents that meet their own policies,and the generated signatures will not leak the identity and policies of the signer.Moreover,the group administrator is allowed to reveal the identity of signer when a controversy occurs.Through the analysis of application scenarios,we concluded that the policy-based group signature needs to meet two essential security properties:simulatability and traceability.And we construct a scheme of policy-based group signature from lattice through techniques such as commitment,zero-knowledge proof,rejection sampling.The security of our scheme is proved to be reduced to the module short integer solution(MSIS)and module learning with errors(MLWE)hard assumptions.Furthermore,we make a performance comparison between our scheme and three lattice-based group signature schemes.The result shows that our scheme has more advantages in storage overhead and the sizes of key and signature are decreased roughly by 83.13%,46.01%,respectively,compared with other schemes.

关键词： Group signature policy-based signature lattice-based cryptography zero-knowledge proof

Immunologic characteristics and predictive risk factors of primary Sj?gren’s syndrome complicated with malignant lymphoma: A multicenter case-control study

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中华医学杂志(英文版) 2024年第10期137卷 2024 Jan 9页

作者： Jin Yuebo Hou linqing li Hao Zhang liyun Peng yuanhong Wang Naidi He Jing Department of Rheumatology and Immunology Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis Peking University People’s Hospital Beijing China Department of Rheumatology and Immunology Linfen Third People’s Hospital Linfen Shanxi China Department of Rheumatology and Immunology Huabei Petroleum Administration Bureau General Hospital Cangzhou Hebei China Affiliated Hospital of North Sichuan Medical College Nanchong Sichuan China

Detection of the foreign object positions in agricultural soils using Mask-RCNN

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International Journal of Agricultural and Biological Engineering 2023年第1期16卷 220-231页

作者： yuanhong li Chaofeng Wang Congyue Wang Xiaoling Deng Zuoxi Zhao Shengde Chen Yubin Lan South China Agricultural University College of Electronic EngineeringGuangzhou 510642China National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides SprayingTechnology(NPAAC)South China Agricultural UniversityGuangzhou 510642China Department of Biological and Agricultural Engineering Texas A&M UniversityCollege StationTX77843USA Guangdong Laboratory for lingnan Modern Agriculture Guangzhou 510642China

Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil.Although many scholars had researched a variety of image-processing techniques,most methods lack robustness.In this study,the deep learning algorithm Mask Region-based Convolutional Neural Network(Mask-RCNN)and a geometric model were combined to improve the GPR positioning accuracy.First,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves.Secondly,a SOIL-GPR geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object sizes.The results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils;then,the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks(FCN);Finally,the operating speed of agricultural machinery was simulated and designed the A-B survey line experiment.The detection accuracy was evaluated by several indicators,such as the survey line direction,soil depth false alarm rate,Mean Average Precision(mAP),and Intersection over Union(IoU).The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively,and the average error of depth prediction is 2.87 cm.The results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters,geometric models,and artificial intelligence algorithms to provide a high-precision and technical solution for the GPR non-destructive detection of soils.

关键词： foreign object soil object position agricultural soil Mask R-CNN GPR image

Vertical distribution characteristics of PM in the surface layer of Guangzhou

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Particuology 2015年第3期13卷 3-9页

作者： Xuejiao Deng Fei li yuanhong li Jianyong li Hongzhi Huang Xiantong liu Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction China Meteorological Administration(CMA) Guangdong Atmosphere Sounds Technology Centre

Measurements of particulate matter （PM）, i.e., PM10, PM2.5, and PM1, have been performed on the Can- ton Tower, a landmark building in Guangzhou, at heights of 121 and 454 m since November 2010, using a GRIMM 180 aerosol particle spectrometer （Germany）. Analyses of data from November 2010 to May 2013 showed that the annual average values of PM10, PM2.5, and PM 1 at the observation height of 121 m above the ground were 44.1, 38,2, and 34.9 μg/m3, respectively, and those at 454m above the ground were 35.7, 30,4, and 27.5 μg/m3, respectively. By considering the values of the secondary concentration limits given in the Ambient Air Quality Standards issued in 2012, it was observed that the annual average values of PM10 at the observation heights of 121 and 454 m, as well as those of PM2.5 at 454 m, reached those standards. Furthermore, the over-standard amplitude of the annual average value of PM2.5 at the observation height of 121 m was 9.1%. During the observation period, the maximum daily average val- ues of PM10, PM2.5, and PMI at the observation height of 121 m were 183.3, 144.8, and 123.8 μg/m3, respectively, and those at 454 m were 102.8, 92.7, and 86.4 μg/m3. The daily average values of PM10 at the observation height of 454 m were not above the standards. The over-standard frequencies of the daily average values of PM10 and PM2.5 at the observation height of 121 m were 0.6% and 10,7% respectively, and the over-standard amplitudes were 9.0% and 24.4%, respectively. The over-standard frequency of the daily average value of PM2.5 at the observation height of 454 m was 2.0%, and the over-standard amplitude was 10.4%. Accordingly, it can be stated that the air at the observation height 454 m above the ground did not reach the secondary limit of the new standards. The pollution was most serious during winter, and the air was relatively cleaner during summer, Overall, the vertical distributions of PM10, PM2.5, and PMI decreased with height. The lapse rates showed the following sequence： PMIO 〉 PM2.5 〉 PM1, which indi

关键词： PM10PM2.5PM10ver-standardVertical distributionSurface layer

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Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

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International Journal of Agricultural and Biological Engineering 2020年第5期13卷 171-180页

作者： yuanhong li Hongjun Wang Weiliang Zhou Zehao Xue Department of Engineering South China Agricultural UniversityGuangzhou 510642China Southern Key Laboratory of Agricultural Equipment Machinery South China Agricultural UniversityGuangzhou 510642China Department of Biological and Agricultural Engineering Texas A&M UniversityCollege StationTX77843USA

Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vision and machine learning algorithms.According to the color characteristics of the targets,to convert the original color picture into YCbCr mode and use the 2D Otsu algorithm to perform gray level image segmentation on the Cb channel.Then the Haar-feature training was carried out.The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms,while its false recognized rate was 3.91%.The strong classifier was formed by weight combination,and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system.The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck.It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature.The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm.Its correct rate can reach 95%.It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.

关键词： Otsu Haar-feature AdaBoost 3D position target pose monocular vision error analysis

Modeling effectiveness and identification of multi-scale objects in farmland soils with improved Yee-FDTD methods

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International Journal of Agricultural and Biological Engineering 2020年第6期13卷 150-158页

作者： yuanhong li Zuoxi Zhao Zhi Qiu Yangfan Luo Yuchan Zhu Department of Engineering South China Agricultural UniversityGuangzhou 510642China Southern Key Laboratory of Agricultural Equipment Machinery South China Agricultural UniversityGuangzhou 510642China Department of Biological and Agricultural Engineering Texas A&M UniversityCollege StationTX77843USA

Finite-Difference Time-Domain(FDTD)is the most popular time-domain approach in computational electromagnetics.Due to the Courant-Friedrich-Levy(CFL)condition and the perfect match layer(PML)boundary precision,FDTD cannot simulate soil medium whose surface is connected by multiple straight lines or curves(multi-scale)accurately and efficiently,which greatly limits the application of FDTD method to simulate buried objects in soils.Firstly,this study proposed the absorption boundary and adopted two typical perfect matching layers(UPML and CPML)to compare their absorption effects,and then using the three forms of improved Yee-FDTD algorithm,alternating-direction implicit(ADI-FDTD),unconditionally stable(US-FDTD)and hybrid implicit explicit finite time-domain(HIE-FDTD)to divide and contrast the boundary model effects.It showed that the HIE-FDTD was suitable for inversion of multi-scale structure object modeling,while ADI-FDTD and US-FDTD were ideal for single-boundary objects in both uniaxial perfectly matched layer(UMPL)and convolution perfectly matched layer(CPML)finite element space.After that,all the models were tested by computer performance for their simulated efficiency.When simulating single boundary objects,UPML-US-FDTD and ADI-FDTD could achieve the ideal results,and in the boundary inversion of multi-scale objects,HIE-FDTD modeling results and efficiency were the best.Test modeling speeds of CPML-HIE-FDTD were compared with three kinds of waveform sources,Ricker,Blackman-Harris and Gaussian.Finally,under the computer condition in which the CPU was i5-8250,the HIE-FDTD model still had better performance than the traditional Yee-FDTD forward modeling algorithm.For modeling multi-scale objects in farmland soils,the methods used CPML combined with the HIE-FDTD were the most efficient and accurate ways.This study can solve the problem that the traditional FDTD algorithm cannot construct non-mesh objects by utilizing the diversity characteristics of Yee cell elements.

关键词： Yee-FDTD multi-scale objects modeling effectiveness Ground Penetrating Radar farmland soils