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• Ethyl acetate and 95% ethanol extracts from T. chinensis rhizomes gave great inhibition on six plant pathogenic fungi • T. chinensis has potential to be a new natural fungicide resource • T. chinensis has potential to be used for the preservation of agricultural and forestry products such as fruits and vegetables The antifungal effectiveness of extracts of five medicinal plant species was determined. The inhibitory activity of extracts of Eucalyptus tereticornis, Xanthium sibiricum, Artemisia argyi, Tupistra chinensis and Pyrola calliantha were evaluated against the mycelial growth of the plant pathogenic fungi Aspergillus niger, Botrytis cinerea, Penicillium digitatum, P. expansum, P. italicum and Rhizopus stolonifer. All plant extracts were prepared at 60°C using solvents (either water, 50% ethanol (v/v), 95% ethanol (v/v), ethyl acetate or petroleum ether). Fungicidal effects of all plants tested were confirmed. Different extracts from the same plant species gave different degrees of inhibition. All aqueous extracts had weak or no activity on all fungi tested. Ethyl acetate and 95% ethanol extracts from T. chinensis rhizomes gave greater inhibition and a broader spectrum inhibition than the other extracts. T. chinensis may have potential as a new natural fungicide and may be used for the preservation of agricultural and forestry products such as fruits and vegetables.

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]

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]

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]

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]

Stable and reliable operation of high-speed railway requires continuous and reliable power supply of traction substation, and isolating switch is one of the most important electrical devices in high-speed railway traction substation. In this paper, to address the issue of isolating switch accurate localization and state recognition simultaneously, we present an automatic isolating switch segmentation and state recognition framework called ISSSR-Net using multitask learning that consists of two stages. First, an isolating switch segmentation network called ISS-Net is proposed for isolating switch pixel-level segmentation precisely, of which a new structure containing a strip pooling module, a channel attention and three pyramid pooling modules is designed to greatly improve the segmentation and recognition performance, even in complex conditions such as rain, snow and fog. Second, to improve state recognition accuracy, the segmentation map yielded from the ISS-Net and the feature map from the shared backbone are together fed into isolating switch recognition network called ISR-Net to recognize its three states. In addition, a global context block is integrated into ISR-Net to further improve state recognition accuracy. Extensive experimental results on a self-collected dataset from Heishan traction substation corroborate that this paper provides an effective and robust method to achieve isolating switch segmentation and state recognition simultaneously. The M IoU and ${F}~1$ -score of segmentation reach 0.93 and 0.94 respectively, which is better than U-Net and its variants, and other SOTA. The ${F}~1$ -score of recognition reach 1.00, which is higher than HOG+SVM and other deep learning methods that have been experimented. [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]

The accelerated digitalization of today’s electric power infrastructure has highlighted the gravity of cyber resilience in the electricity ecosystem. Due to the expanded attack surface inherited in the digitalized operational and information technologies, the electricity sector is facing a continually escalating cyberthreat landscape and must act to prepare for more frequent and sophisticated cyberattacks as the new normal. To aid in this effort, this paper presents a novel approach to identify critical branches to strengthen and by doing so, shield the smart-grid power system from the threat of load redistribution attacks (LRAs) under a wide range of operating scenarios. Compared with conventional critical branch identification approaches, we propose a new concept, namely, chain of defense, that empowers the system operator to incorporate existing cyber protections and develop branch strengthening strategy in a more dynamic, adaptive, and flexible way. We then propose a novel reinforcement learning framework to identify the most effective chain of defense. A cross-updating search strategy is developed to specifically ensure that the identified chain of defense can safeguard the network from the two-fold damaging effects of the LRAs on operation economics and security simultaneously. The effectiveness of the proposed approach is evaluated on the IEEE 14-bus system and the European 89-bus system. Simulation results indicate that the proposed approach provides more comprehensive and effective mitigation against the damaging effects of LRAs compared with the conventional approaches. [ABSTRACT FROM AUTHOR]

Automatic inspections of overhead contact lines (OCLs) are developed to implement anomaly detection during normal operation. It is an essential prerequisite for efficient maintenance of railway electrification system. This paper presents a comprehensive survey on the inspections of OCLs with an emphasis on computer vision technology, which has developed rapidly due to its ability to understand images. Our survey begins with a brief introduction on anomalies in OCL inspections and generic procedures of computer vision inspection for anomaly detection. Subsequently, for detecting deviations of parameters and defective components during OCL inspections, the existing techniques involving stereo vision and object vision, especially convolution neural networks are described in detail from two aspects: measurement of OCL parameters and identification of OCL conditions. Some interference factors in OCL inspection are analyzed. Actual cases of the inspections are also briefly shown. Challenges and suggestions for further research on OCL inspection are drawn toward the end of the paper. [ABSTRACT FROM AUTHOR]

This letter develops a new bilevel optimization model to construct false data injection attack based on pre- and post- dispatch. In order to enhance the attack concealment, the proposed bilevel model can minimize the variation of uploaded measurements between pre- and post-attack before dispatching, after which the attack can lead the system to an uneconomic and insecure operating state after dispatching. Simulation results validate the effectiveness of the proposed bilevel model in term of operating cost and network overloads. [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 swivel clevis (SC) is a vulnerable part of the Overhead Catenary System (OCS). Regular inspection using computer vision technology is an effective way to detect SC defects and improve the OCS operation safety. However, achieving full automation of SC defects detection is still a difficult task due to defective sample scarcity and data distribution shift. To overcome these problems, this paper proposes a novel defects detection method that combines an adaptive SC segmentation network (Adaptive SSN) and local operators. During the inspection process, an unreliability index defined by the model uncertainty and prior knowledge is used to monitor the reliability of the Adaptive SSN. When data distribution shift causes the Adaptive SSN to be unreliable, human annotator will be asked to update the training set and retrain the Adaptive SSN to adapt to the new data distribution. Then the geometric features obtained from segmentation masks and the local features extracted by local operators are used to detect the SC defects. Effectiveness of the proposed method is demonstrated by the experimental results on the data from several high-speed railway lines. [ABSTRACT FROM AUTHOR]

The catenary insulator maintains electrical insulation between catenary and ground. Its defects may happen due to the long-term impact from vehicle and environment. At present, the research of defect detection for catenary insulator faces several challenges. 1) Localization accuracy is low, which causes the localized object to be incomplete or/and merge with unnecessary background. 2) Horizontal localization brings inevitable unnecessary information because horizontal box cannot fit well with the shape of insulator. 3) Supervised learning models for defects recognition are unreliable as the available defect samples are insufficient to train models well. To address these issues, this article proposes a novel two-stage defect detection method. In the localization stage, a novel localization network called TOL-Framework is constructed to reduce the background and realize tighter oriented localization. Compared with general basic framework Faster R-CNN, the TOL-Framework cascades a regression module inside basic framework and adds an external postprocess network, which is adversarially trained by standard insulators to refine the localization. These two novel steps greatly improve the oriented localization accuracy. In the defect detection stage, an adversarial reconstruction model that is trained only using normal samples is proposed to evaluate the defect states. A comparison with other methods is conducted using a dataset collected from a 60km section of the Changsha-Zhuzhou railway line in China. The results show the proposed method has the highest localization accuracy, and is effective for insulator defect detection. [ABSTRACT FROM AUTHOR]

Actual condition-based maintenance for traction power supply equipment (TPSE) is almost based on completely observable equipment state. However, it is unpractical to accurately reveal the equipment state due to the inescapably uncertainty of state assessment. In order to optimize the maintenance of TPSE, a maintenance model based on partially observable Markov decision process is proposed in this paper. Firstly, the degradation process of the TPSE is described by a four-state Markov process, and the state residence time and its transition probability of the equipment are obtained by equaling fault times in the statistical period. Then, the imperfect maintenance is considered in this paper. And the failure risk of the TPSE after maintenance is quantified for optimizing both the economic cost and the reliability of maintenance strategy. Finally, the practical fault record data of 27.5 kV vacuum circuit breakers for a traction power supply system (TPSS) are used to verify the proposed model. The results show that the maintenance model can provide guidance on decision-making for the maintenance under uncertainty, and the determination of maintenance schemes to optimize both TPSE reliability and operational cost. [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]

Gray leaf spot (GLS), caused by the fungal pathogen Cercospora zeina (C. zeina), is one of the most destructive soil-borne diseases in maize (Zea mays L.), and severely reduces maize production in Southwest China. However, the mechanism of resistance to GLS is not clear and few resistant alleles have been identified. Two maize inbred lines, which were shown to be resistant (R6) and susceptible (S8) to GLS, were injected by C. zeina spore suspensions. Transcriptome analysis was carried out with leaf tissue at 0, 6, 24, 144, and 240 h after inoculation. Compared with 0 h of inoculation, a total of 667 and 419 stable common differentially expressed genes (DEGs) were found in the resistant and susceptible lines across the four timepoints, respectively. The DEGs were usually enriched in 'response to stimulus' and 'response to stress' in GO term analysis, and 'plant–pathogen interaction', 'MAPK signaling pathways', and 'plant hormone signal transduction' pathways, which were related to maize's response to GLS, were enriched in KEGG analysis. Weighted-Genes Co-expression Network Analysis (WGCNA) identified two modules, while twenty hub genes identified from these indicated that plant hormone signaling, calcium signaling pathways, and transcription factors played a central role in GLS sensing and response. Combing DEGs and QTL mapping, five genes were identified as the consensus genes for the resistance of GLS. Two genes, were both putative Leucine-rich repeat protein kinase family proteins, specifically expressed in R6. In summary, our results can provide resources for gene mining and exploring the mechanism of resistance to GLS in maize. [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]

Chromatin in eukaryotes folds into a complex three-dimensional (3D) structure that is essential for controlling gene expression and cellular function and is dynamically regulated in biological processes. Studies on plant phosphorus signaling have concentrated on single genes and gene interactions. It is critical to expand the existing signaling pathway in terms of its 3D structure. In this study, low-Pi treatment led to greater chromatin volume. Furthermore, low-Pi stress increased the insulation score and the number of TAD-like domains, but the effects on the A/B compartment were not obvious. The methylation levels of target sites (hereafter as RdDM levels) peaked at specific TAD-like boundaries, whereas RdDM peak levels at conserved TAD-like boundaries shifted and decreased sharply. The distribution pattern of RdDM sites originating from the Helitron transposons matched that of genome-wide RdDM sites near TAD-like boundaries. RdDM pathway genes were upregulated in the middle or early stages and downregulated in the later stages under low-Pi conditions. The RdDM pathway mutant ddm1a showed increased tolerance to low-Pi stress, with shortened and thickened roots contributing to higher Pi uptake from the shallow soil layer. ChIP-seq results revealed that ZmDDM1A could bind to Pi- and root development-related genes. Strong associations were found between interacting genes in significantly different chromatin-interaction regions and root traits. These findings not only expand the mechanisms by which plants respond to low-Pi stress through the RdDM pathway but also offer a crucial framework for the analysis of biological issues using 3D genomics., (© 2023 Society for Experimental Biology and John Wiley & Sons Ltd.)

A previous metabolomic and genome-wide association analysis of maize screened a glucose-6-phosphate 1-epimerase ( ZmG6PE ) gene, which responds to low-phosphorus (LP) stress and regulates yield in maize's recombinant inbred lines (RILs). However, the relationship of ZmG6PE with phosphorus and yield remained elusive. This study aimed to elucidate the underlying response mechanism of the ZmG6PE gene to LP stress and its consequential impact on maize yield. The analysis indicated that ZmG6PE required the Aldose_epim conserved domain to maintain enzyme activity and localized in the nucleus and cell membrane. The zmg6pe mutants showed decreased biomass and sugar contents but had increased starch content in leaves under LP stress conditions. Combined transcriptome and metabolome analysis showed that LP stress activated plant immune regulation in response to the LP stress through carbon metabolism, amino acid metabolism, and fatty acid metabolism. Notably, LP stress significantly reduced the synthesis of glucose-1-phosphate, mannose-6-phosphate, and β-alanine-related metabolites and changed the expression of related genes. ZmG6PE regulates LP stress by mediating the expression of ZmSPX6 and ZmPHT1.13 . Overall, this study revealed that ZmG6PE affected the number of grains per ear, ear thickness, and ear weight under LP stress, indicating that ZmG6PE participates in the phosphate signaling pathway and affects maize yield-related traits through balancing carbohydrates homeostasis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhang, Luo, Liu, Jin, Zhang, Zhong, Li, Hu, Wang, Ali, Riaz, Sahito, Iqbal, Zhang, Liu, Wu, Gao, Gao, Su and Gao.)

Gibberella ear rot (GER) in maize caused by Fusarium graminearum is one of the most devastating maize diseases reducing grain yield and quality worldwide. The genetic bases of maize GER resistance remain largely unknown. Using artificial inoculation across multiple environments, the GER severity of an association panel consisting of 316 diverse inbred lines was observed with wide phenotypic variation. In the association panel, a genome-wide association study using a general linear model identified 69 single-nucleotide polymorphisms (SNPs) significantly associated with GER resistance at the threshold of 2.04 × 10 -5 , and the average phenotypic variation explained (PVE) of these SNPs was 5.09%. We also conducted a genome-wide association study analysis using a mixed linear model at a threshold of 1.0 × 10 -4 , and 16 significantly associated SNPs with an average PVE of 4.73% were detected. A combined general linear model and mixed linear model method obtained 10 co-localized significantly associated SNPs linked to GER resistance, including the most significant SNP (PZE-105079915) with the greatest PVE value, 9.07%, at bin 5.05 following 10 candidate genes. These findings are significant for the exploration of the complicated genetic variations in maize GER resistance. The regions and genes identified herein provide a list of candidate targets for further investigation, in addition to the elite germplasm resources that can be used for breeding GER resistance in maize., Competing Interests: The author(s) declare no conflict of interest.

Nutrient restrictions and large-scale emergence of diseases are threatening the maize production. Recent findings demonstrated that there is a certain synergistic interaction between nutrition and diseases pathways in model plants, however there are few studies on the synergistic genes of nutrients and diseases in maize. Thus, the transcriptome data of nitrogen (N) and phosphorus (P) nutrients and diseases treatments in maize, rice, wheat and Arabidopsis thaliana were collected in this study, and four and 22 weighted co-expression modules were obtained by using Weighted Gene Co-expression Network Analysis (WGCNA) in leaf and root tissues, respectively. With a total of 5252 genes, MFUZZ cluster analysis screened 26 clusters with the same expression trend under nutrition and disease treatments. In the meantime, 1427 genes and 22 specific consensus quantitative trait loci (scQTLs) loci were identified by meta-QTL analysis of nitrogen and phosphorus nutrition and disease stress in maize. Combined with the results of cluster analysis and scQTLs, a total of 195 consistent genes were screened, of which six genes were shown to synergistically respond to nutrition and disease both in roots and leaves. Moreover, the six candidate genes were found in scQTLs associated with gray leaf spot (GLS) and corn leaf blight (CLB). In addition, subcellular localization and bioinformatics analysis of the six candidate genes revealed that they were primarily expressed in endoplasmic reticulum, mitochondria, nucleus and plasma membrane, and were involved in defense and stress, MeJA and abscisic acid response pathways. The fluorescence quantitative PCR confirmed their responsiveness to nitrogen and phosphorus nutrition as well as GLS treatments. Taken together, findings of this study indicated that the nutrition and disease have a significant synergistic response in maize., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Luo, Li, Li, Zhang, Yu, Zhang, Zhang, Sahito, Zhang, Liu, Wu, Gao, Gao and Gao.)

Lead (Pb) is a highly toxic contaminant to living organisms and the environment. Excessive Pb in soils affects crop yield and quality, thus threatening human health via the food chain. Herein, we investigated Pb tolerance among a maize association panel using root bushiness (BSH) under Pb treatment as an indicator. Through a genome-wide association study of relative BSH, we identified four single nucleotide polymorphisms (SNPs) and 30 candidate genes associated with Pb tolerance in maize seedlings. Transcriptome analysis showed that four of the 30 genes were differentially responsive to Pb treatment between two maize lines with contrasting Pb tolerance. Among these, the ZmbZIP107 transcription factor was confirmed as the key gene controlling maize tolerance to Pb by using gene-based association studies. Two 5' UTR_variants in ZmbZIP107 affected its expression level and Pb tolerance among different maize lines. ZmbZIP107 protein was specifically targeted to the nucleus and ZmbZIP107 mRNA showed the highest expression in maize seedling roots among different tissues. Heterologous expression of ZmbZIP107 enhanced rice tolerance to Pb stress and decreased Pb absorption in the roots. Our study provided the basis for revelation of the molecular mechanism underlying Pb tolerance and contributed to cultivation of Pb-tolerant varieties in maize., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hou, Liu, Zhang, Zou, Yuan, Gao, Zhang, Pan, Ma and Shen.)

A deficiency in the macronutrient phosphate (Pi) brings about various changes in plants at the morphological, physiological and molecular levels. However, the molecular mechanism for regulating Pi homeostasis in response to low-Pi remains poorly understood, particularly in maize ( Zea mays L.), which is a staple crop and requires massive amounts of Pi. Therefore, in this study, we performed expression profiling of the shoots and roots of maize seedlings with Pi-tolerant genotype at both the transcriptomic and proteomic levels using RNA sequencing and isobaric tags for relative and absolute quantitation (iTRAQ). We identified 1944 differentially expressed transcripts and 340 differentially expressed proteins under low-Pi conditions. Most of the differentially expressed genes were clustered as regulators, such as transcription factors involved in the Pi signaling pathway at the transcript level. However, the more functional and metabolism-related genes showed expression changes at the protein level. Moreover, under low-Pi conditions, Pi transporters and phosphatases were specifically induced in the roots at both the transcript and protein levels, and increased amounts of mRNA and protein of two purple acid phosphatases (PAPs) and one UDP-sulfoquinovose synthase (SQD) were specifically detected in the roots. The new insights provided by this study will help to improve the P-utilization efficiency of maize.