ZmDRR206 functions in maintaining cell wall integrity during maize seedling growth and defense response to external stresses

Plants adaptively change their cell wall composition and structure during their growth,development,and interactions with environmental stresses.Dirigent proteins(DIRs)contribute to environmental adaptations by dynamically reorganizing the cell wall and/or by generating defense compounds.A maize DIR,ZmDRR206,was previously reported to play a dominant role in regulation of storage nutrient accumulation in endosperm during maize kernel development.Here we show that ZmDRR206 mediates maize seedling growth and disease resistance by coordinately regulating biosynthesis of cell wall components for cell-wall integrity(CWI)maintenance.Expression of ZmDRR206 was induced in maize seedlings upon pathogen infection.ZmDRR206 overexpression in maize resulted in reduced seedling growth and photosynthetic activity but increased disease resistance and drought tolerance,revealing a tradeoff between growth and defense.Consistently,ZmDRR206 overexpression reduced the contents of primary metabolites and down-regulated genes involved in photosynthesis,while increasing the contents of major cell wall components,defense phytohormones,and defense metabolites,and up-regulated genes involved in defense and cell-wall biosynthesis in seedlings.ZmDRR206-overexpressing seedlings were resistant to cell-wall stress imposed by isoxaben,and ZmDRR206 physically interacted with ZmCesA10,which is a cellulose synthase unit.Our findings suggest a mechanism by which ZmDRR206 coordinately regulates biosynthesis of cell-wall components for CWI maintenance during maize seedling growth,and might be exploited for breeding strong disease resistance in maize.

基于ITA值的皮肤颜色影响因素分析

ITA值是基于CIE L*a*b-颜色空间建立的皮肤颜色测量指标,目前广泛应用于皮肤颜色研究和化妆品功效评测中,本文旨在对基于ITA值的导致皮肤发黑暗沉、发红、发黄的影响因素进行梳理综述。本文以构成ITA值的亮白、红-绿色相、黄-蓝色相三个因素为分类依据,对导致皮肤发黑暗沉、发红、发黄的影响因素进行了梳理分析,总结了各因素的生化机制及部分通路交叉点。依此提出一些改善皮肤颜色的建议及“以点带面”的护肤设想。ITA值在肤色评定和化妆品功效评价方面的广泛认可及其良好的发展前景,更加重视其在肤色发黑发黄发红细分类方面的应用意义。

CCT family genes in cereal crops: A current overview

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.

Predicting the Results of RNA Molecular Specific Hybridization Using Machine Learning

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.

Inhibition of the spread of endophytic Sporisorium reilianum renders maize resistance to head smut

Head smut, caused by the fungal pathogen Sporisorium reilianum, poses a grave threat to maize(Zea mays) production worldwide. Here we report cytological and molecular evidence for maize resistance to head smut. During early stages of root infection, S. reilianum mycelium was capable of penetrating the root epidermis of both resistant(Ji1037) and susceptible(HZ4) inbred lines. S. reilianum hyphae were observed in the root–stem junction at 6 days after inoculation. In an attempt to monitor hyphal spread within the maize plant,a highly specific and sensitive real-time PCR method was established to estimate the hyphal content in infected maize tissues. During the upward growth of endophytic S.reilianum, the extent of hyphal spread was markedly different between Ji1037 and HZ4. Very little or no pathogen was detected in aerial parts of Ji1037, whereas large amounts of pathogen accumulated in aboveground tissues, particularly inflorescences, of HZ4. Thus,maize resistance to S. reilianum was achieved mainly by inhibition of endophytic hyphal growth rather than by prevention of early-root penetration by the pathogen.

A Novel Method for Detecting Disk Filtration Attacks via the Various Machine Learning Algorithms

Disk Filtration(DF) Malware can attack air-gapped computers. However, none of the existing technique can detect DF attacks. To address this problem, a method for detecting the DF attacks based on the fourteen Machine Learning(ML) algorithms is proposed in this paper. First, we collect a number of data about Power Spectral Density(PSD) and frequency of the sound wave from the Hard Disk Drive(HDD). Second, the corresponding machine learning models are trained respectively using the collected data. Third, the trained ML models are employed to detect whether a DF attack occurs or not respectively, if given pair of values of PSD and frequency are input. The experimental results show that the max accuracy of detection is greater than or equal to 99.4%.

基于采样和加权损失函数的模型窃取攻击方法

模型窃取攻击旨在获得一个和目标受害模型功能相似的替代模型.现有的方法主要采用数据生成或数据选择方法和交叉熵损失函数去获得一个较好的攻击效果.据此,本文着重研究了攻击过程中这两个极为重要的模块:数据采样和损失函数.同时,本文提出了一个新颖的模型窃取攻击方法S&W,其包含了一种新的采样策略和一个精心设计的加权损失函数.首先,新的采样策略更加关注于从受害者模型中获得更多信息的重要样本.与此同时,本文通过引入k-Center算法达到选择样本的多样性的目的.其次,受到经典Focal损失函数的启发,本文设计了一种新的加权损失函数.该损失函数主要关注于受害者模型和替代模型对于相同输入所给出的输出之间的差异,从而促使替代模型模拟受害者模型.在4个常用的数据集上,我们通过实验证明了本文提出的方法的有效性.相比于之前最好的方法,本文方法最高有5.03%的性能提升.

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

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.

VIS+AI: integrating visualization with artificial intelligence for efficient data analysis

Visualization and artificial intelligence(AI)are well-applied approaches to data analysis.On one hand,visualization can facilitate humans in data understanding through intuitive visual representation and interactive exploration.On the other hand,AI is able to learn from data and implement bulky tasks for humans.In complex data analysis scenarios,like epidemic traceability and city planning,humans need to understand large-scale data and make decisions,which requires complementing the strengths of both visualization and AI.Existing studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as VIS4AI.However,how can AI and visualization complement each other and be integrated into data analysis processes are still missing.In this paper,we define three integration levels of visualization and AI.The highest integration level is described as the framework of VIS+AI,which allows AI to learn human intelligence from interactions and communicate with humans through visual interfaces.We also summarize future directions of VIS+AI to inspire related studies.

A survey of urban visual analytics:Advances and future directions

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.

Quantitative disease resistance:Multifaceted players in plant defense

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.

Multi-omics analysis reveals the pivotal role of phytohormone homeostasis in regulating maize grain water content

Grain water content(GWC)is a key determinant for mechanical harvesting of maize(Zea mays).In our previous research,we identified a quantitative trait locus,qGWC1,associated with GWC in maize.Here,we examined near-isogenic lines(NILs)NILL and NILH that differed at the qGWC1 locus.Lower GWC in NILL was primarily attributed to reduced grain water weight(GWW)and smaller fresh grain size,rather than the accumulation of dry matter.The difference in GWC between the NILs became more pronounced approximately 35 d after pollination(DAP),arising from a faster dehydration rate in NILL.Through an integrated analysis of the transcriptome,proteome,and metabolome,coupled with an examination of hormones and their derivatives,we detected a marked decrease in JA,along with an increase in cytokinin,storage forms of IAA(IAA-Glu,IAA-ASP),and IAA precursor IPA in immature NILL kernels.During kernel development,genes associated with sucrose synthases,starch biosynthesis,and zein production in NILL,exhibited an initial up-regulation followed by a gradual down-regulation,compared to those in NILH.This discovery highlights the crucial role of phytohormone homeostasis and genes related to kernel development in balancing GWC and dry matter accumulation in maize kernels.

基于强化学习的舰载机保障作业实时调度方法

衡量航母作战性能的重要指标是舰载机出动架次率,而影响舰载机出动架次率的关键因素是舰载机保障作业调度效率.舰载机保障作业调度是指在有限时间、空间和资源约束的前提下合理安排舰载机所需保障作业顺序并高效完成舰载机的作业保障.现有基于最优化方法(动态规划、线性规划等)和启发式方法(如遗传算法、粒子群等)的求解策略仅适用于保障作业可预知情况下的作业调度,很难满足高动态作战场景下的实时保障作业调度需求.基于此,本文提出了一种新的基于DQN(deep Q-network)的舰载机保障作业实时调度方法,将舰载机保障作业调度问题建模成部分可观测马尔科夫决策过程(partially observable Markov decision processes)问题,利用全局与长期收益对保障作业调度过程进行优化,并通过离线学习和在线调配的学习决策框架进行解决.经过仿真实验验证,该方法能显著提高舰载机保障作业调度效率并满足实时决策环境的需要.

The Auxin-Regulated Protein ZmAuxRPI Coordinates the Balance between Root Growth and Stalk Rot Disease Resistance in Maize

To optimize fitness, plants must efficiently allocate their resources between growth and defense. Although phytohormone crosstalk has emerged as a major player in balancing growth and defense, the genetic basis by which plants man age this balance remai ns elusive. We previously ide ntified a quantitative disease . resistance locus, qRfg2, in maize (Zea mays) that protects against the fungal disease Gibberella stalk rot. Here, through map-based cloning, we demonstrate that the causal gene at qRfg2 is ZmAuxRPI, which encodes a plastid stroma-localized auxin-regulated protein. ZmAuxRPI responded quickly to pathogen challenge with a rapid yet transient reduction in expression that led to arrested root growth but enhanced resista nee to Gibberella stalk rot and Fusarium ear rot. ZmAuxRPI was show n to promote the biosynthesis of indole-3-acetic acid (IAA), while suppressing the formation of benzoxazinoid defense compounds. ZmAuxRPI presumably acts as a resource regulator modulating indole-3-glycerol phosphate and/or indole flux at the branch point between the IAA and benzoxazinoid biosynthetic pathways. The concerted interplay between IAA and benzoxazinoids can regulate the growth-defense balance in a timely and efficient manner to optimize plant fitness.

An Atypical Thioredoxin Imparts Early Resistance to Sugarcane Mosaic Virus in Maize

Sugarcane mosaic virus （SCMV） causes substantial losses of grain yield and forage biomass in susceptible maize worldwide. A major quantitative trait locus, Scmvl, has been identified to impart strong resistance to SCMV at the early infection stage. Here, we demonstrate that ZmTrxh, encoding an atypical h-type thioredoxin, is the causal gene at Scmvl, and that its transcript abundance correlated strongly with maize resistance to SCMV. ZmTrxh alleles, whether they are resistant or susceptible, share the identical coding/proximal promoter regions, but vary in the upstream regulatory regions. ZmTrxh lacks two canon- ical cysteines in the thioredoxin active-site motif and exists uniquely in the maize genome. Because of this, ZmTrxh is unable to reduce disulfide bridges but possesses a strong molecular chaperone-like activity. ZmTrxh is dispersed in maize cytoplasm to suppress SCMV viral RNA accumulation. Moreover, ZmTrxh- mediated maize resistance to SCMV showed no obvious correlation with the salicylic acid- and jasmonic acid-related defense signaling pathways. Taken together, our results indicate that ZmTrxh exhibits a distinct defense profile in maize resistance to SCMV, differing from previously characterized dominant or recessive potyvirus resistance genes.

Fine Mapping of RppP25, a Southern Rust Resistance Gene in Maize

Southern rust （Puccinia polysora Underw.） is a major disease that can cause severe yield losses in maize （Zea mays L.）. In our previous study, a major gene RppP25 that confers resistance to southern rust was identified in inbred line P25. Here, we report the fine mapping and candidate gene analysis of RppP25 from the near-isogenic line F939, which harbors RppP25 in the genetic background of the susceptible inbred line F349. The inheritance of resistance to southern rust was investigated in the BC1F1 and BC3F1 populations, which were derived from a cross between F939 and F349 （as the recurrent parent）. The 1：1 segregation ratio of resistance to susceptible plants in these two populations indicated that the resistance is controlled by a single dominant gene. Ten markers, including three simple sequence repeat （SSR） markers and seven insertion/deletion （InDel） markers, were developed in the RppP25 region. RppP25 was delimited to an interval between P091 and M271, with an estimated length of 40 kb based on the physical map of B73. In this region, a candidate gene was identified that was predicted to encode a putative nucleotide-binding site leucine-rich repeat （NBS-LRR） protein. Two co-segregated markers will aid in pyramiding diverse southern rust resistance alleles into elite materials, and thereby improve southern rust resistance worldwide.

A Sequential Quantitative Trait Locus Fine-Mapping Strategy Using Recombinant-Derived Progeny

A thorough understanding of the quantitative trait loci （QTLs） that underlie agronomically important traits in crops would greatly increase agricultural productivity. Although advances have been made in QTL cloning, the majority of QTLs remain unknown because of their low heritability and minor contributions to phenotypic performance. Here we summarize the key advantages and disad- vantages of current QTL fine-mapping methodologies, and then introduce a sequential QTL fine-mapping strategy based on both genotypes and phenotypes of progeny derived from recombinants. With this mapping strategy, experimental errors could be dramat- ically diminished so as to reveal the authentic genetic effect of target QTLs. The number of progeny required to detect QTLs atvarious R2 values was calculated, and the backcross generation suitable to start QTL fine-mapping was also estimated. This mapping strategy has proved to be very powerful in narrowing down QTL regions, particularly minor-effect QTLs, as revealed by fine-mapping of various resistance QTLs in maize. Application of this sequential QTL mapping strategy should accelerate cloning of agronomically important QTLs, which is currently a substantial challenge in crops.

Combined genome-wide association study and transcriptome analysis reveal candidate genes for resistance to Fusarium ear rot in maize

Fusarium ear rot,caused by Fusarium verti-cillioides,is a devastating fungal disease in maize that re-duces yield and quality;moreover,F.verticillioides pro-duces fumonisin mycotoxins,which pose serious threats to human and animal health.Here,we performed a genome-wide association study(GWAS)under three en-vironmental conditions and identified 34 single-nucleotide polymorphisms(SNPs)that were significantly associated with Fusarium ear rot resistance.With reference to the maize B73 genome,69 genes that overlapped with or were adjacent to the significant SNPs were identified as potential resistance genes to Fusarium ear rot.Comparing transcriptomes of the most resistant and most susceptible lines during the very early response to Fusarium ear rot,we detected many differentially expressed genes enriched for pathways related to plant immune responses,such as plant hormone signal transduction,phenylpropanoid bio-synthesis,and cytochrome P450 metabolism.More than one-fourth of the potential resistance genes detected in the GWAS were differentially expressed in the tran-scriptome analysis,which allowed us to predict numbers of candidate genes for maize resistance to ear rot,in-cluding genes related to plant hormones,a MAP kinase,a PR5-like receptor kinase,and heat shock proteins.We propose that maize plants initiate early immune responses to Fusarium ear rot mainly by regulating the growth-defense balance and promoting biosynthesis of defense compounds.

The Genetic and Molecular Basis of Plant Resistance to Pathogens

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.

Quantitative Disease Resistance： Dissection and Adoption in Maize

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.