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Quantitative disease resistance:Multifaceted players in plant defense

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Journal of Integrative Plant Biology 2023年第2期65卷 594-610页

作者： Mingyue Gou Peter Balint-Kurti Mingliang XU Qin Yang State Key Laboratory of Wheat and Maize Crop Science Collaborative Innovation Center of Henan Grain CropsCenter for Crop Genome EngineeringCollege of AgronomyHenan Agricultural UniversityZhengzhou 450002China The Shennong Laboratory Zhengzhou 450002China Department of Entomology and Plant Pathology North Carolina State UniversityRaleighNC 27695USA Plant Science Research Unit USDA-ARSRaleighNC 27695USA State Key Laboratory of Plant Physiology and Biochemistry National Maize Improvement CenterCenter for Crop Functional Genomics and Molecular BreedingCollege of AgronomyChina Agricultural UniversityBeijing 100193China State Key Laboratory of Crop Stress Biology for Arid Areas Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region of the Ministry of AgricultureCollege of AgronomyNorthwest A&F UniversityYangling 712100China

In contrast to large-effect qualitative disease resistance,quantitative disease resistance(QDR)exhibits partial and generally durable resistance and has been extensively utilized in crop breeding.The molecular mechanisms underlying QDR remain largely unknown but considerable progress has been made in this area in recent years.In this review,we summarize the genes that have been associated with plant QDR and their biological functions.Many QDR genes belong to the canonical resistance gene categories with predicted functions in pathogen perception,signal transduction,phytohormone homeostasis,metabolite transport and biosynthesis,and epigenetic regulation.However,other"atypical"QDR genes are predicted to be involved in processes that are not commonly associated with disease resistance,such as vesicle trafficking,molecular chaperones,and others.This diversity of function for QDR genes contrasts with qualitative resistance,which is often based on the actions of nucleotidebinding leucine-rich repeat(NLR)resistance proteins.An understanding of the diversity of QDR mechanisms and of which mechanisms are effective against which classes of pathogens will enable the more effective deployment of QDR to produce more durably resistant,resilient crops.

关键词： biological function durable resistance plant defense quantitative disease resistance(QDR)

Dysfunctional gene splicing in glucose metabolism may contribute to Alzheimer’s disease

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Chinese Medical Journal 2023年第6期136卷 666-675页

作者： Shengfeng Deng Peng Yi Mingliang XU Qian Yi Jianguo Feng Laboratory of Anesthesiology Department of Anesthesiologythe Affiliated Hospital of Southwest Medical UniversityLuzhouSichuan 646000China Department of Physiology School of Basic Medical SciencesSouthwest Medical UniversityLuzhouSichuan 646000China Affiliated Xinhui Hospital Southern Medical University(People’s Hospital of Xinhui District)JiangmenGuangdong 529100China

The glucose metabolism is crucial for sustained brain activity as it provides energy and is a carbon source for multiple biomacromolecules;glucose metabolism decreases dramatically in Alzheimer’s disease(AD)and may be a fundamental cause for its development.Recent studies reveal that the alternative splicing events of certain genes effectively regulate several processes in glucose metabolism including insulin receptor,insulin-degrading enzyme,pyruvate kinase M,receptor for advanced glycation endproducts,and others,thereby,influencing glucose uptake,glycolysis,and advanced glycation end-products-mediated signaling pathways.Indeed,the discovery of aberrant alternative splicing that changes the proteomic diversity and protein activity in glucose metabolism has been pivotal in our understanding of AD development.In this review,we summarize the alternative splicing events of the glucose metabolism-related genes in AD pathology and highlight the crucial regulatory roles of splicing factors in the alternative splicing process.We also discuss the emerging therapeutic approaches for targeting splicing factors for AD treatment.

关键词： Alzheimer’s disease Alternative splicing Glucose metabolism Splicing factors

A survey of urban visual analytics:Advances and future directions

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Computational Visual Media 2023年第1期9卷 3-39页

作者： Zikun Deng Di Weng Shuhan Liu Yuan Tian Mingliang XU Yingcai Wu State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Microsoft Research Asia Beijing 100080China School of Information Engineering Zhengzhou UniversityZhengzhouChina Henan Institute of Advanced Technology Zhengzhou UniversityZhengzhou 450001China

Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models.Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities.To promote further academic research and assist the development of industrial urban analytics systems,we comprehensively review urban visual analytics studies from four perspectives.In particular,we identify 8 urban domains and 22 types of popular visualization,analyze 7 types of computational method,and categorize existing systems into 4 types based on their integration of visualization techniques and computational models.We conclude with potential research directions and opportunities.

关键词： visual analytics smart city spatiotemporal data analysis urban analytics

CCT family genes in cereal crops: A current overview

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The Crop Journal 2017年第6期5卷 449-458页

作者： Yipu Li Mingliang XU National Maize Improvement Center of China China Agricultural University Beijing 100193 China

Control of flowering time is crucial for reproductive success of cereal crops, and has a significant impact on grain yield as well as adaptation to diverse environmental conditions.Plants integrate signals from both environmental cues and endogenous regulatory pathways to fine-tune flowering time. The CCT domain originally described to a 43-amino acid sequence at the C-terminus of three Arabidopsis proteins, namely CONSTANS(CO),CO-LIKE, and TIMING OF CAB1(TOC1). The CCT domain-containing genes(CCT genes),which encode transcription co-factors, are the major genetic determinants that modulate flowering time, and this in turn enables plants to effectively expand their territory to take advantage of favorable habitats. Moreover, certain CCT genes have pleiotropic effects on morphological traits and confer resistance/tolerance to biotic/abiotic stresses. CCT genes can be classified into three families, namely COL(CONSTANS-like), PRR(Pseudo-response regulator), and CMF(CCT motif family),based on their non-CCT domains. During domestication, natural and artificial selection resulted in reduced nucleotide diversity of CCT genes in modern cultivated cereals than their wild types. Here, we review the features and functions of CCT genes in cereal crops and propose future research to focus on CCT genes and their utilization in crop breeding.

关键词： CCT domain Flowering time Photoperiod Pleiotropy

Quantitative Disease Resistance： Dissection and Adoption in Maize

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Molecular Plant 2017年第3期10卷 402-413页

作者： Qin Yang Peter Balint-Kurti Mingliang XU Department of Entomology and Plant Pathology Noah Carolina State University Raleigh NC 27895 USA National Maize Improvement Centre of China China Agricultural University Beijing 100193 People's Republic of China USDA-ARS Plant Sciences Research Unit Department of Entomology and Plant Pathology North Carolina State University Raleigh NC 27695 USA

Maize is the world＇s most produced crop, providing food, feed, and biofuel. Maize production is constantly threatened by the presence of devastating pathogens worldwide. Characterization of the genetic compo- nents underlying disease resistance is a major research area in maize which is highly relevant for resistance breeding programs. Quantitative disease resistance （QDR） is the type of resistance most widely used by maize breeders. The past decade has witnessed significant progress in fine-mapping and cloning of genes controlling QDR. The molecular mechanisms underlying QDR remain poorly understood and exploited. In this review we discuss recent advances in maize QDR research and strategy for resistance breeding.

关键词： quantitative disease resistance maize gene

Robust least squares projection twin SVM and its sparse solution

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Journal of Systems Engineering and Electronics 2023年第4期34卷 827-838页

作者： ZHOU Shuisheng ZHANG Wenmeng CHEN Li xu mingliang School of Mathematics and Statistics Xidian UniversityXi’an 710126China School of Physical Education Zhengzhou UniversityZhengzhou 450001China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China

Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsity.Therefore,it is difficult for LSPTSVM to process large-scale datasets with outliers.In this paper,we propose a robust LSPTSVM model(called R-LSPTSVM)by applying truncated least squares loss function.The robustness of R-LSPTSVM is proved from a weighted perspective.Furthermore,we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space.Finally,the sparse R-LSPTSVM algorithm(SR-LSPTSVM)is proposed.Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly.

关键词： outliers robust least squares projection twin support vector machine(R-LSPTSVM) low-rank approximation sparse solution

Unsupervised pseudoinverse hashing learning model for rare astronomical object retrieval

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Science China(Technological Sciences) 2022年第6期65卷 1338-1348页

作者： WANG Ke GUO Ping LUO ALi xu mingliang Artificial Intelligence Research Center School of Computer and Artificial IntelligenceZhengzhou UniversityZhengzhou 450001China School of Systems Science Beijing Normal UniversityBeijing 100875China Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100012China

Searching for rare astronomical objects based on spectral data is similar to finding needles in a haystack owing to their rarity and the immense data volume gathered from large astronomical spectroscopic surveys.In this paper,we propose a novel automated approximate nearest neighbor search method based on unsupervised hashing learning for rare spectra retrieval.The proposed method employs a multilayer neural network using autoencoders as the local compact feature extractors.Autoencoders are trained with a non-gradient learning algorithm with graph Laplace regularization.This algorithm also simplifies the tuning of network architecture hyperparameters and the learning control hyperparameters.Meanwhile,the graph Laplace regularization can enhance the robustness by reducing the sensibility to noise.The proposed model is data-driven;thus,it can be viewed as a general-purpose retrieval model.The proposed model is evaluated in experiments and real-world applications where rare Otype stars and their subclass are retrieved from the dataset obtained from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope(Guo Shoujing Telescope).The experimental and application results show that the proposed model outperformed the baseline methods,demonstrating the effectiveness of the proposed method in rare spectra retrieval tasks.

关键词： compact features unsupervised hashing object retrieval pseudoinverse learning

Predicting the Results of RNA Molecular Specific Hybridization Using Machine Learning

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1384-1396页

作者： Weijun Zhu Xiaokai Liu Mingliang XU Huanmei Wu School of Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Mathematics and Statistics Zhengzhou UniversityZhengzhou 450001China School of Informatics and Computing Indiana University-Purdue University IndianapolisIndianapolisIN 46074 USA

Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide,within an acceptable time,whether a specific RNA hybridization is effective.We hereby introduce a machine learning based technique to address this problem.Sample machine learning(ML)models tested in the training phase include algorithms based on the boosted tree(BT)random forest(RF),decision tree(DT)and logistic regression(LR),and the corresponding models are obtained.Given the RNA molecular coding training and testing sets,the trained machine learning models are applied to predict the classification of RNA hybridization results.The experiment results show that the op timal predictive accuracies are 96.2%,96.6%,96.0%and 69.8%for the RF,BT,DT and LR-based approaches,respectively,un der the strong constraint condition,compared with traditiona representative methods.Furthermore,the average computation efficiency of the RF,BT,DT and LR-based approaches are208679,269756,184333 and 187458 times higher than that o existing approach,respectively.Given an RNA design,the BT based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effective ness of molecular hybridization.

关键词： Biological effectiveness boosted tree(BT) decision tree(DT) random forest(RF) RNA design specific hybridization logistic regression(LR)

The Genetic and Molecular Basis of Plant Resistance to Pathogens

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Journal of Genetics and Genomics 2013年第1期40卷 23-35页

作者： Yan Zhang Thomas Lubberstedt Mingliang XU National Maize Improvement Center of China China Agricultural University Iowa State University Department of Agronomy1204 Agronomy Hall

Plant pathogens have evolved numerous strategies to obtain nutritive materials from their host, and plants in turn have evolved the preformed physical and chemical barriers as well as sophisticated two-tiered immune system to combat pathogen attacks. Genetically, plant resistance to pathogens can be divided into qualitative and quantitative disease resistance, conditioned by major gene（s） and multiple genes with minor effects, respectively. Qualitative disease resistance has been mostly detected in plant defense against biotrophic pathogens, whereas quantitative disease resistance is involved in defense response to all plant pathogens, from biotrophs, hemibiotrophs to necrotrophs. Plant resistance is achieved through interception of pathogen-derived effectors and elicitation of defense response. In recent years, great progress has been made related to the molecular basis underlying host--pathogen interactions. In this review, we would like to provide an update on genetic and molecular aspects of plant resistance to pathogens.

关键词： Resistance to pathogen Innate immune system R genes