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The rigid catenary system is widely used in tunnels to power electric trains via contact with a pantograph. Due to gravity, the contact wire normally has a sag that may affect the dynamic interaction performance with a pantograph. To reduce the contact wire sag, the most efficient measure is to improve the moment of inertia of the conductor rail, which is used to clamp the contact wire. Six new types of conductor rail with large moments of inertia are developed based on a conventional conductor rail. Then both the static and dynamic analyses are conducted to investigate the performance of the new types of conductor rail with a big moment of inertia. The conductor rail's 3D solid finite element model is built using a finite element approach. The vertical deflection and the stress distribution are comparatively analyzed among different types of conductor rail. The analysis results indicate that the vertical deflection and maximum stress are significantly reduced when using the conductor rail with a large moment of inertia. The best performance is observed when the conductor rail of case 1 is used. The maximum sag is reduced by 28.37%, and the maximum stress is decreased by 27.76% compared with the conventional conductor. Finally, a pantograph model is included to evaluate the dynamic performance of the conductor rail with large moments of inertia. The results indicate that contact force fluctuation is significantly reduced after the conductor rails with large moments of inertia are presented. The conductor rail of case 1 shows the best performance, which can reduce the contact force standard deviation by 32% and 27% at speeds of 160 km/h and 200 km/h. [ABSTRACT FROM AUTHOR]

A variety of Chinese textual operational text data has been recorded during the operation and maintenance of the high-speed railway catenary system. Such defect text records can facilitate defect detection and defect severity analysis if mined efficiently and accurately. Therefore, in this context, this paper focuses on a specific problem in defect text mining, which is to efficiently extract defect-relevant information from catenary defect text records and automatically identify catenary defect severity. The specific task is transformed into a machine learning problem for defect text classification. First, we summarize the characteristics of catenary defect texts and construct a text dataset. Second, we use BERT to learn defect texts and generate word embedding vectors with contextual features, fed into the classification model. Third, we developed a deep text categorization network (DTCN) to distinguish the catenary defect level, considering the contextualized semantic features. Finally, the effectiveness of our proposed method (BERT-DTCN) is validated using a catenary defect textual dataset collected from 2016 to 2018 in the China Railway Administration in Chengdu, Lanzhou, and Hengshui. Moreover, BERT-DTCN outperforms several competitive methods in terms of accuracy, precision, recall, and F 1 -score value. [ABSTRACT FROM AUTHOR]

Eddy-current analysis and losses estimation are significant components in the optimized design of the tractive transformer. The conventional analytical method forces to linearize the nonlinear magnetic property and as an important quantity in the calculation, the permeability is only acquired dependent on the empirical method. Furthermore, the computational precision is often difficult to assure in losses estimation when distorted excitation originated from nonlinear magnetized relation in electrical steel is neglected. In our study, the eddy-current field problem is delicately solved by analytical deduction, in which the nonlinear constitutive relation toward media and the harmonic component in the excitation source are sufficiently considered. Compared with the accuracy of the traditional formula, the proposed model achieves better accuracy on eddy-current losses with finite-element analysis, especially when the operating condition is closed to magnetic saturation. [ABSTRACT FROM AUTHOR]

Overhead contact lines (OCLs) are electric transmission lines that power railways, which are constantly threatened by external weather and environmental factors due to their outdoor location. Hence, for the long-term functioning of railway lines, a weather-driven risk predictor is an essential tool. Current prediction methods mainly adopt a single-point estimation system with fixed weights of neural networks and therefore cannot propagate the uncertainties within the data and model, resulting in unreliable predictions. To enhance safety-risk prevention capabilities, this paper proposes an uncertainty-aware trustworthy weather-driven failure-risk approach for OCLs, in a probabilistic deep multitask learning framework. Firstly, a deep Gaussian process is employed as the backbone model to deal with imbalanced weather-related failure datasets with limited fault samples. Moreover, a multi-task learning framework is embedded to simultaneously predict the multiple weather-driven failure risks under lightning, wind and haze. Finally, the predictive uncertainty is decomposed into epistemic and aleatory uncertainties, where epistemic and aleatory uncertainties account for the uncertainty within the model and data, respectively. Extensive experiments on actual OCLs are carried out to demonstrate the effectiveness of the proposed approach, which can effectively capture the predictive uncertainty and provide trustworthy predictive decisions of mitigating operational risk for railway operators. • Propose an uncertainty-aware trustworthy WDFR prediction approach in an MTL4DGP framework for OCLs. • Develop an MTL formulation for DGP in a two-layer structure. • Formulate an uncertainty decomposition scheme which investigates predictive uncertainty from epistemic and aleatory perspectives. [ABSTRACT FROM AUTHOR]

• A two-layer energy management strategy based on fuzzy control for high-speed railway hybrid energy storage system is proposed. • Discharge threshold can be adaptively adjusted with power and lifespan of hybrid energy storage system. • The energy management system intelligently allocates power according to the state of charge of each subsystem and the traction power. • This strategy reduces the average energy extraction power and extends the life of the energy storage system. In order to extend the service life of the high-speed railway hybrid energy storage system and reduce the power shock impact of the traction network, an energy management strategy based on double-layer fuzzy logic control is proposed. This strategy can dynamically adjust the discharge threshold according to the external power and the remaining life of the energy storage system, and dynamically allocate power according to the real-time state of charge. At the same time, the strategy uses the rain flow counting method to complete the extraction of the depth of discharge and establishes an equivalent life evaluation model. The simulation results show that this energy management strategy can effectively reduce the frequency and depth of charging and discharging, and reduce the load impact of the traction network. Compared with the threshold-based energy management strategy, the life loss of this method can be reduced by more than 50%, which effectively improves the service life of the energy storage system. [ABSTRACT FROM AUTHOR]

Onboard cable is important electrical equipment of an electric locomotive. In order to effectively evaluate the aging state of the cable, this article proposes a method to evaluate the aging state of the cable based on a new method of dielectric spectrum data processing and a new structure model of recognition algorithm. First, the log-min-max gray-scale (LMMGS) method is proposed, which transforms the dielectric parameters based on the frequency-domain dielectric spectrum (FDS) test into the gray matrix. The correlation among different dielectric parameters is retained, and the interference of their order of magnitude difference on feature extraction is reduced. Then, the new structure of spatial pyramid pooling (SPP) is improved by rectangular mesh dividing named Rectangular-SPP, which increases the number of divisible layers of the spatial pyramid, and it is used to optimize convolutional neural network (CNN), so as to remove the limitation of recognition algorithm on the size of input data and expand the scale of feature extraction. Finally, a set of methods for evaluating the aging state of onboard cable is proposed. The experimental results show that this method can effectively distinguish the insulation aging state of onboard cable. Meanwhile, the proposed Rectangular-SPP-CNN algorithm has a better performance than the conventional machine learning algorithms. [ABSTRACT FROM AUTHOR]

As a key component of the high-speed railway catenary, the insulator plays an important role in supporting the catenary and maintaining the insulation between the catenary and earth. Regular inspection using computer vision technology is an effective way to detect insulator defects and improve the catenary operation safety. However, achieving full automation of insulator defects detection is still a difficult task due to the defective sample scarcity and subtle defect features. To overcome the problem, this article proposes defect detection GANs (DefGANs) that consist of a denoising autoencoder, a discriminator, and a classifier to detect defects, which can generate defective samples from pitted latent representations, thereby improving the reliability of defect detection classifier. The proposed DefGAN can pit the latent representations of normal samples to generate defective samples. Our proposal mainly consists of two stages. In the first stage, we use cascaded deep segmentation networks (CDSNets) to extract the insulator from the catenary images. Then, the insulator area is sampled into small patches. In the second stage, the patches are fed into the proposed DefGAN to detect defects. Moreover, the defect score is determined by the anomaly probability of the classifier and reconstruction error of the denoising autoencoder. The effectiveness of our work is measured across the catenary data sets. [ABSTRACT FROM AUTHOR]

• Propose three weather-driven failure probability prediction techniques. • Develop a failure probability model that integrates internal triggers and external weather-driven hazard factors. • Establish a risk propagation network for OCLs to capture the dependencies between risk factors and risk consequences. • Propose a data-driven integrated predictive probabilistic risk analytic in a dynamic Bayesian network framework for OCLs. Due to completely working under open-air conditions without backup equipment, the overhead contact lines (OCLs) are suffering from external extreme weather conditions, except for the long-term dynamic vibrations of catenary-pantograph system. These risk factors are prone to cause failures of OCL components and power outages, which may further result in transportation interruptions, enormous economic losses, serious social impacts, and even catastrophic safety accidents. To comprehensively investigate the associated risks in OCLs, a data-driven integrated predictive probabilistic risk analytics framework based on dynamic Bayesian network is proposed to identify the significant risk factors and analyse the time-dependant failure patterns in dynamic risk propagation network of OCLs. After exploiting the weather-driven analytics for failure probability prediction, an integrated failure probability modelling for OCL components is developed, simultaneously incorporating internal, and weather-driven hazard factors of OCLs. A predictive risk metric is suggested based on the newly established risk propagation network of OCLs, which can account for weather hazards, system failures, financial costs, and social trust losses, in response to external weather conditions over time. Numerical studies conducted on the actual OCLs demonstrate that the proposed framework can dynamically model and evaluate risks of failure patterns that expose to OCLs. [ABSTRACT FROM AUTHOR]

• Propose a probabilistic lightning model, depicting uncertain features in occurrence and intensity of lightning strike. • Propose a spatiotemporal fragility model, investigating spatiotemporal dependencies between LI and failure probability of OCLs. • Develop a data-driven BN approach for predicting lightning-related failure risk of OCLs. Lightning-related failures are of great concerns for the reliable performance of overhead contact lines (OCLs) of high-speed railway. Predicting lightning-related failure probability is valuable to capture the recurrence of OCL failures due to lightning strike and enable predictive maintenance decision-making. In this paper, a data-driven Bayesian network (BN) approach with spatiotemporal fragility model is developed to investigate the dependencies between lightning strike and OCL failures, and predict lightning-related failure risk of OCLs. It consists of three critical components, (1) a probabilistic lightning model that integrates multiple key lightning parameters is proposed to capture the uncertainty in the occurrence and intensity of lightning strike; (2) a spatiotemporal fragility model of OCL corridor is presented to examine the impacts of lightning strike on OCL failure probability; (3) furthermore, the Bayesian network is embedded with above-mentioned two models to predict lightning-related failure risk of OCLs, improving its robustness. Compared with other advanced prediction methods, the proposed approach achieves better prediction performance with high accuracy over imbalanced dataset. In addition, it can still work acceptably on noisy lightning data with a signal-to-noise ratio of 15dB or higher. [ABSTRACT FROM AUTHOR]

This study focuses on developing a comprehensive model of a rigid overhead system, which includes essential components such as the suspension structure, positioning clamp, and expansion joint. The modelling approach utilizes finite element theory and beam elements to accurately represent the displacement, stiffness, and mass characteristics of the system. The models also incorporate the suspension structure and positioning line clamp, which play crucial roles in suspending and positioning the busbar. Various suspension structures and positioning line clamps are evaluated based on their dynamic characteristics. The expansion joint, responsible for connecting different anchor sections of the rigid overhead system, undergoes a detailed analysis. Different assembly scenarios, including ideal and deflected assembly conditions, are considered. To simulate the dynamic behaviour of the expansion joint, additional beams are introduced into the system model. The primary finding of the analysis is that the maximum stresses observed in the constructed expansion joint model, under different temperature conditions and normal/deflected assembly conditions, remain within the permissible stress limits of the material. This indicates a high level of safety. However, certain areas exhibit stress concentration, particularly at the sliding block B and sliding rod A positions. This stress concentration is primarily attributed to the unique assembly form of the expansion joint. To improve stress distribution and enhance service reliability, the analysis suggests optimizing the installation deflection angle and geometric design of the expansion joint. Furthermore, the concentrated mass at the expansion joint significantly impacts the current collection quality of the pantograph-overhead system. Mitigating this negative impact can be achieved by reducing the mass of the expansion joint. [ABSTRACT FROM AUTHOR]

• Select optimal candidates based on spatiotemporal similarity check. • Develop a probabilistic wind model, accounting for the uncertainty in wind variables. • Design an environment sensitive parameter for floater intrusion. • Propose a wind-caused floater intrusion risk prediction approach for OCLs. Wind-caused floater intrusion has posed enormous threats to the safety and resilience of overhead contact lines (OCLs) of electrified railway. In this paper, a Bayesian neural network (BNN) based prediction model is proposed, incorporating spatiotemporal correlations, uncertainty of extreme wind speed and direction, characteristic parameters of OCLs, environmental information and human factors into floater intrusion risk prediction. To select optimal candidates, the spatial-temporal correlation among wind data with respect to different OCL corridors are examined. Then, the probabilistic wind model is developed to capture the stochastic nature of wind events and account for the uncertainty in wind speed and direction. The spatiotemporal correlation-constrained environment sensitive parameter is formulated to investigate the impacts of wind, characteristic parameters of OCLs, environmental information and human factors on floater intrusion of OCLs. A BNN model is implemented into predicting wind-caused floater intrusion risk. Finally, the remarkable effectiveness and robustness of the proposed model are compared with some other advanced prediction methods. The experimental results demonstrate that the proposed model not only has the capability of uncertainty estimation, but also provides the confidence interval of floater intrusion risk prediction, which can play a significant role in preventive operational flexibility and resilience against weather-related risks. [ABSTRACT FROM AUTHOR]

• Investigate challenges of imbalanced dataset with limited fault samples and certain noise. • Propose an uncertainty-aware trustworthy FHPF risk prediction approach in a robust DGP framework for OCLs. • Develop an uncertainty decomposition scheme that estimates prediction uncertainty from epistemic and aleatory perspectives. Due to completely open-air operation, fog-haze has become the main cause of contamination on the insulator surface of overhead contact lines (OCLs), further leading to pollution flashover and a series of serious risk consequences. To perceive the fog-haze-caused pollution flashover (FHPF) risk of OCL insulator, a robust deep Gaussian process (DGP)-based uncertainty-aware trustworthy prediction approach is proposed, incorporating epistemic and aleatoric uncertainties. In particular, aiming at the imbalanced dataset with limited fault samples, the prediction of FHPF risk is cast as a classification problem, and solved by DGP using stochastic gradient Hamiltonian Monte Carlo (SGHMC) inference. The key parameters are identified investigating the influences of fog-haze on insulator surface contamination. Furthermore, the SGHMC sampling-based inference is utilized to efficiently capture the intractable posterior distribution, dealing with uncertainty and enhancing the flexibility of the prediction approach. Finally, extensive experiments on high-speed railway line validate the effectiveness and superior of the proposed approach, compared to other advanced predictive classification methods. In addition, it cannot only capture the prediction uncertainty over a limited number of fault samples, but also achieve favorable prediction performance under unseen noisy environments, ultimately ensuring robust and trustworthy FHPF risk predictions for OCLs. [ABSTRACT FROM AUTHOR]

Key message: Combined GWAS, WGCNA, and gene-based association studies identified the co-expression network and hub genes for maize EC induction. ZmARF23 bound to ZmSAUR15 promoter and regulated its expression, affecting EC induction. Embryonic callus (EC) induction in immature maize embryos shows high genotype dependence, which limits the application of genetic transformation in transgenic breeding and gene function elucidation in maize. Herein, we conducted a genome-wide association mapping (GWAS) for four EC induction-related traits, namely rate of embryonic callus induction (REC), increased callus diameter (ICD), ratio of shoot formation (RSF), and length of shoot (LS) across different environments. A total of 77 SNPs were significantly associated these traits under three environments and using the averages (across environments). Among these significant SNPs, five were simultaneously detected under multiple environments and 11 had respective phenotypic variation explained > 10%. A total of 257 genes were located in the linkage disequilibrium decay of these REC- and ICD-associated SNPs, of which 178 were responsive to EC induction. According to the expression values of the 178 genes, we performed a weighted gene co-expression network analysis (WGCNA) and revealed an EC induction-associated module and five hub genes. Hub gene-based association studies uncovered that the intragenic variations in GRMZM2G105473 and ZmARF23 influenced EC induction efficiency among different maize lines. Dual-luciferase reporter assay indicated that ZmARF23 bound to the promoter of a known causal gene (ZmSAUR15) for EC induction and positively regulated its expression on the transcription level. Our study will deepen the understanding of genetic and molecular mechanisms underlying EC induction and contribute to the use of genetic transformation in maize. [ABSTRACT FROM AUTHOR]

Lead (Pb) in the soil affects the growth and development of plants and causes damages to the human body through the food chain. Here, we identified and cloned a Pb-tolerance gene ZmPIP2;5 based on a weighted gene co-expression network analysis and gene-based association studies. We showed that ZmPIP2;5 encodes a plasma membrane aquaporin and positively regulated Pb tolerance and accumulation in Arabidopsis and yeast. Overexpression of Z mPIP2;5 increased root length and fresh weight of Arabidopsis seedlings under Pb stress. Heterologous expression of ZmPIP2;5 in yeast caused the enhanced growth speed under Pb treatment and Pb accumulation in yeast cells. A (T/A) SNP in the ZmPIP2;5 promoter affected the expression abundance of ZmPIP2;5 and thereby led to the difference in Pb tolerance among different maize lines. Our study helps to understand the mechanism underlying plant tolerance to Pb stress and provides new ideas for breeding Pb-tolerance maize varieties via molecular marker-assisted selection. • ZmPIP2;5 was identified using a gene-based association analysis. • ZmPIP2;5 was verified to positively regulate the Pb tolerance. • T/A variation in the promoter of ZmPIP2;5 led to the difference of Pb tolerance. • Increased ZmPIP2;5 promoted maize adaption to Pb stress. • ZmPIP2;5 improved plant tolerance to Pb probably by maintaining water balance. [ABSTRACT FROM AUTHOR]

Key message: Combined linkage and association analysis revealed five co-localized genetic loci across multiple environments. The key gene Zm00001d026491 was further verified to influence leaf length by candidate gene association analysis. Leaf morphology and number determine the canopy structure and thus affect crop yield. Herein, the genetic basis and key genes for 25 leaf-related traits, including leaf lengths (LL), leaf widths (LW), and leaf areas (LA) of eight continuous leaves under the tassel, and the number of leaves above the primary ear (LAE), were dissected by using an association panel and a biparental population. Using an intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, 290 quantitative trait loci (QTL) controlling these traits were detected across different locations, among which 115 QTL were individually repeatedly identified in at least two environments. Using the association panel, 165 unique significant single-nucleotide polymorphisms (SNPs) were associated with target traits (P < 2.15E-06), of which 35 were separately detected across multiple environments. In total, 42 pleiotropic QTL/SNPs (pQTL/SNPs) were responsible for at least two of the LL, LW, LA, and LAE traits across multiple environments. Combining the QTL mapping and association study, five unique SNPs were located within the confidence intervals of seven QTL, and 77 genes were identified based on the linkage disequilibrium regions of co-localized SNP loci. Gene-based association studies verified that the intragenic variants in the candidate gene Zm00001d026491 influenced LL of the third leaf counted from the top node. These findings will provide vital information to understanding the genetic basis of leaf-related traits and help to cultivate maize varieties with ideal plant architecture. [ABSTRACT FROM AUTHOR]

Ear shank length (ESL) has significant effects on grain yield and kernel dehydration rate in maize. Herein, linkage mapping and genome-wide association study were combined to reveal the genetic architecture of maize ESL. Sixteen quantitative trait loci (QTL) were identified in the segregation population, among which five were repeatedly detected across multiple environments. Meanwhile, 23 single nucleotide polymorphisms were associated with the ESL in the association panel, of which four were located in the QTL identified by linkage mapping and were designated as the population-common loci. A total of 42 genes residing in the linkage disequilibrium regions of these common variants and 12 of them were responsive to ear shank elongation. Of the 12 genes, five encode leucine-rich repeat receptor-like protein kinases, proline-rich proteins, and cyclin11, respectively, which were previously shown to regulate cell division, expansion, and elongation. Gene-based association analyses revealed that the variant located in Cyclin11 promoter affected the ESL among different lines. Cyclin11 showed the highest expression in the ear shank 15 days after silking among diverse tissues of maize, suggesting its role in modulating ESL. Our study contributes to the understanding of the genetic mechanism underlying maize ESL and genetic modification of maize dehydration rate and kernel yield. [ABSTRACT FROM AUTHOR]

Salinization seriously threatens the normal growth of maize, especially at the seedling stage. Recent studies have demonstrated that circular RNAs (circRNAs) play vital roles in the regulation of plant stress resistance. Here, we performed a genome-wide association study (GWAS) on the survival rate of 300 maize accessions under a salt stress treatment. A total of 5 trait-associated SNPs and 86 candidate genes were obtained by the GWAS. We performed RNA sequencing for 28 transcriptome libraries derived from 2 maize lines with contrasting salt tolerance under normal and salt treatment conditions. A total of 1217 highly expressed circRNAs were identified, of which 371 were responsive to a salt treatment. Using PCR and Sanger sequencing, we verified the reliability of these differentially expressed circRNAs. An integration of the GWAS and RNA-Seq analyses uncovered two differentially expressed hub genes (Zm00001eb013650 and Zm00001eb198930), which were regulated by four circRNAs. Based on these results, we constructed a regulation model of circRNA/miRNA/mRNA that mediated salt stress tolerance in maize. By conducting hub gene-based association analyses, we detected a favorable haplotype in Zm00001eb198930, which was responsible for high salt tolerance. These results help to clarify the regulatory relationship between circRNAs and their target genes as well as to develop salt-tolerant lines for maize breeding. [ABSTRACT FROM AUTHOR]

SUMMARY: The ability of immature maize (Zea mays) embryos to form embryonic calluses (ECs) is highly genotype dependent, which limits transgenic breeding development in maize. Here, we report the association map‐based cloning of ZmSAUR15 using an association panel (AP) consisting of 309 inbred lines with diverse formation abilities for ECs. We demonstrated that ZmSAUR15, which encodes a small auxin‐upregulated RNA, acts as a negative effector in maize EC induction. Polymorphisms in the ZmSAUR15 promoter that influence the expression of ZmSAUR15 transcripts modulate the EC induction capacity in maize. ZmSAUR15 is involved in indole‐3‐acetic acid biosynthesis and cell division in immature embryo‐derived callus. The ability of immature embryos to induce EC formation can be improved by the knockout of ZmSAUR15, which consequently increases the callus regeneration efficiency. Our study provides new insights into overcoming the genotypic limitations associated with EC formation and improving genetic transformation in maize. Significance Statement: Embryonic callus formation is highly genotype dependent in maize, which largely limits maize transgenic breeding development. This study found that ZmSAUR15 negatively effects embryonic callus formation in maize. The polymorphisms in the ZmSAUR15 promoter influence its expression. Furthermore, ZmSAUR15 modulates indole‐3‐acetic acid biosynthesis and cell division in callus. [ABSTRACT FROM AUTHOR]

The discovery and characterization of the opaque endosperm gene provide ideas and resources for the production and application of maize. We found an o213 mutant whose phenotype was opaque and shrunken endosperm with semi-dwarf plant height. The protein, lipid, and starch contents in the o213 endosperm were significantly decreased, while the free amino acid content in the o213 endosperm significantly increased. The aspartic acid, asparagine, and lysine contents were raised in the o213 endosperm by 6.5-, 8.5-, and 1.7-fold, respectively. Genetic analysis showed that this o213 mutant is a recessive single-gene mutation. The position mapping indicated that o213 is located in a 468-kb region that contains 11 protein-encoding genes on the long arm of chromosome 5. The coding sequence analysis of candidate genes between the WT and o213 showed that ZmYSL2 had only a single-base substitution (A–G) in the fifth exon, which caused methionine substitution to valine. Sequence analysis and the allelic test showed that o213 is a new mutant allele of ZmYSL2. The qRT-PCR results indicated that o213 is highly expressed in the stalks and anthers. Subcellular localization studies showed that o213 is a membrane transporter. In the variation analysis of o213, the amplification of 65 inbred lines in GWAS showed that this 3-bp deletion of the first exon of o213 was found only in temperate inbred lines, implying that the gene was artificially affected in the selection process. Our results suggest that o213 is an important endosperm development gene and may serve as a genetic resource. [ABSTRACT FROM AUTHOR]

Key message: Two read depth methods were jointly used in next-generation sequencing data to identify deletions in maize population. GWAS by deletions were analyzed for gene expression pattern and classical traits, respectively. Many studies have confirmed that structural variation (SV) is pervasive throughout the maize genome. Deletion is one type of SV that may impact gene expression and cause phenotypic changes in quantitative traits. In this study, two read count approaches were used to analyze the deletions in the whole-genome sequencing data of 270 maize inbred lines. A total of 19,754 deletion windows overlapped 12,751 genes, which were unevenly distributed across the genome. The deletions explained population structure well and correlated with genomic features. The deletion proportion of genes was determined to be negatively correlated with its expression. The detection of gene expression quantitative trait loci (eQTL) indicated that local eQTL were fewer but had larger effects than distant ones. The common associated genes were related to basic metabolic processes, whereas unique associated genes with eQTL played a role in the stress or stimulus responses in multiple tissues. Compared with the eQTL detected by SNPs derived from the same sequencing data, 89.4% of the associated genes could be detected by both markers. The effect of top eQTL detected by SNPs was usually larger than that detected by deletions for the same gene. A genome-wide association study (GWAS) on flowering time and plant height illustrated that only a few loci could be consistently captured by SNPs, suggesting that combining deletion and SNP for GWAS was an excellent strategy to dissect trait architecture. Our findings will provide insights into characteristic and biological function of genome-wide deletions in maize. [ABSTRACT FROM AUTHOR]

Seed germination directly affect maize yield and grain quality. Low-temperature reduces maize yield by affecting seed germination and seedling growth. However, the molecular mechanism of maize seed germination under low-temperature remains unclear. In this study, the transcriptome data of two maize inbred lines SCL127 (chilling-sensitive) and SCL326 (chilling-tolerant) were analyzed at five time points (0 H, 4 H, 12 H, 24 H, and 48 H) under low-temperature conditions. Through the comparison of SCL127-0 H-vs-SCL326-0 H (Group I), SCL127-4 H-vs-SCL326-4 H (Group Ⅱ), SCL127-12 H-vs-SCL326-12 H (Group Ⅲ), SCL127-24 H-vs-SCL326-24 H (Group Ⅳ), and SCL127-48 H-vs SCL326-48 H (Group Ⅴ), a total of 8,526 differentially expressed genes (DEGs) were obtained. Weighted correlation network analysis revealed that Zm00001d010445 was the hub gene involved in seed germination under low-temperature conditions. Zm00001d010445 -based association analysis showed that Hap Ⅱ (G) was the excellent haplotype for seed germination under low-temperature conditions. These findings provide a new perspective for the study of the genetic architecture of maize tolerance to low-temperature and contribute to the cultivation of maize varieties with low-temperature tolerance. • RNA-seq was performed on two maize lines under low-temperature. • Zm00001d010445 was identified to affect maize germination. [ABSTRACT FROM AUTHOR]

Aluminum (Al) toxicity is one of the primary factors limiting crop production in acid soils worldwide. The cell wall is the major target of Al toxicity owing to the presence of many Al binding sites. Previous studies have found that XTH , encoding xyloglucan endohydrolase (XEH) and xyloglucan endotransglucosylase (XET), could participate in cell wall extension and affect the binding ability of the cell wall to Al by impeding the activities of these two enzymes. In this study, we found that ZmXTH , an XTH gene in maize, was involved in Al detoxification. The Al-induced up-regulation of ZmXTH occurred in the roots, prominently in the root tips. Additionally, the expression of ZmXTH was specifically induced by Al3+ but no other divalent or trivalent cations. Compared with the wild-type Arabidopsis , ZmXTH overexpressing plants grew more healthy and had decreased Al content in their root and root cell wall after Al stress. Overall, the results suggest that ZmXTH could confer the Al tolerance of transgenic Arabidopsis plants by reducing the Al accumulation in their roots and cell walls. [ABSTRACT FROM AUTHOR]

• ZmMATE6 gene from maize was cloned and transformed into Arabidopsis thaliana. • Overexpression of ZmMATE6 in Arabidopsis resulted in enhancing the resistance to Al. • Transgenic Arabidopsis exhibited increased citrate exudation and decreased Al accumulation in roots. • The loss of function caused by the substitution of the fifth residue of CEM domain may also be related to other factors. The yields of cereal crops grown on acidic soils are often reduced by aluminum (Al) toxicity because the prevalence of toxic Al3+ cations increases as pH falls below 5.0. The Al-dependent release of citrate from resistant lines of maize is controlled by ZmMATE1 which encodes a multidrug and toxic compound extrusion (MATE) transporter protein. ZmMATE6 is another member of this family in maize whose expression is also increased by Al treatment. We investigated the function of this gene in more detail to determine whether it also contributes to Al resistance. Quantitative RT-PCR measurements found that ZmMATE6 was expressed in the roots and leaves of Al-resistant and sensitive inbred lines. Treatment with Al induced ZmMATE6 expression in all tissues but several other divalent or trivalent cations tested had no effect on expression. This expression pattern and the induction by Al treatment was confirmed in ZmMATE6 promoter–β-glucuronidase fusion lines. Heterogeneous expression of ZmMATE6 displayed a greater Al-activated release of citrate from the roots and was significantly resistant to Al toxicity than controls. This was associated with reduced accumulation of Al in the root tissues. Our results demonstrated that ZmMATE6 expression is induced by Al and functions as a citrate transporter. [ABSTRACT FROM AUTHOR]