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The γ-aminobutyric acid (GABA) is a widely distributed neurotransmitter in living organisms, known for its inhibitory role in animals. GABA exerts calming effects on the mind, lowers blood pressure in animals, and enhances stress resistance during the growth and development of plants. Enhancing GABA content in plants has become a focal point of current research. In plants, GABA is synthesized through two metabolic pathways, the GABA shunt and the polyamine degradation pathway, with the GABA shunt being the primary route. Extensive studies have investigated the regulatory mechanisms governing GABA synthesis. At the genetic level, GABA production and degradation can be modulated by gene overexpression, signaling molecule-induced expression, transcription factor regulation, and RNA interference. Additionally, at the level of transporter proteins, increased activity of GABA transporters and proline transporters enhances the transport of glutamate and GABA. The activity of glutamate decarboxylase, a key enzyme in GABA synthesis, along with various external factors, also influences GABA synthesis. This paper summarizes the biological functions, metabolic pathways, and regulatory mechanisms of GABA, providing a theoretical foundation for further research on GABA in plants.

The production of amylose is facilitated by granule-bound starch synthase (GBSS). Despite its importance, the specific protein interactions involving barley grain-bound starch synthase Ia (HvGBSSIa) remain poorly understood. To elucidate this, we engineered a pET-32a-HvGBSSIa prokaryotic expression vector for specific expression in E. coli Rosetta cells. A rabbit anti-HvGBSSIa polyclonal antibody was generated and employed to enrich HvGBSSIa-binding proteins from barley grains through immunoprecipitation. The isolated complexes were then resolved through SDS-PAGE, and the constituent proteins were identified using mass spectrometry coupled with database searches. Our results confirmed the successful preparation of a highly specific polyclonal antibody against HvGBSSI. Furthermore, differential expression of HvGBSSIa was assessed across various barley tissues and developmental stages of the grain, revealing peak expression at 25 days post-flowering. Proteins interacting with HvGBSSIa, including sucrose synthase and starch branching enzyme, were identified through co-immunoprecipitation. This study lays the groundwork for further detailed analyses of the HvGBSSIa protein complex in barley.

ADP-glucose pyrophosphorylase (AGPase), which is a key enzyme in the starch biosynthesis pathway, plays a critical role in barley grain development. Despite its importance, the regulatory mechanisms governing AGPase expression, particularly the influence of plant hormones, remain poorly understood in barley. To address this, we identified and characterized the HvAGPS2b gene, which encodes the AGPase small subunit. The full-length HvAGPS2b gene was cloned from the barley database and expressed as a recombinant protein using the pET-30a system. Polyclonal antibodies were prepared against HvAGPS2b to facilitate detailed analysis. Our findings revealed that HvAGPS2b, as a small subunit of the rate-limiting enzyme AGPase, is integral to the later stages of grain development. Furthermore, RT-qPCR and Western blotting analyses showed that the phytohormones ABA, GA, ETH, and BR significantly upregulated the expression of AGPase small subunits. These results underscore the vital role of plant hormones in modulating AGPS2b expression, thereby influencing grain development. This study provides significant insights into the hormonal regulation of starch biosynthesis and establishes a foundation for further investigation into the functional dynamics of AGPase in barley.

Sheath blight (ShB) of maize, as a soil-borne disease caused by Rhizoctonia solani AG1-IA, is one of the main obstacles for maintaining the sustainable production of maize. R. solani has a wide host range and low-resistance sources, there is a lack of resistant resources against ShB, and the damage caused by ShB cannot be effectively controlled. To effectively protect crops against ShB, it is necessary to combine information about its pathogenicity and about the disease prevention and control of the pathogenic fungus and to identify areas that require more in-depth research. While progress has been made in the identification of disease-related genes in corn and pathogens, their mechanisms remain unclear. Research related to disease control involves the use of agronomic methods, chemical control, biological control, and genetic improvement. Breeding and identification of high-resistant maize varieties are key and difficult points in the control of maize sheath blight. This article reviews the research progress of the symptoms, the pathogen’s biological characteristics, the infection process, the pathogenic mechanism, and comprehensive control of maize sheath blight disease and provides future research directions of maize sheath blight in China. It aims to provide reliable technical routes and research ideas for future crop-disease-resistance research, especially to sheath blight of maize.

Starch phosphorylase (PHO) is a pivotal enzyme within the GT35-glycogen–phosphorylase (GT; glycosyltransferases) superfamily. Despite the ongoing debate surrounding the precise role of PHO1, evidence points to its substantial influence on starch biosynthesis, supported by its gene expression profile and subcellular localization. Key to PHO1 function is the enzymatic regulation via phosphorylation; a myriad of such modification sites has been unveiled in model crops. However, the functional implications of these sites remain to be elucidated. In this study, we utilized site-directed mutagenesis on the phosphorylation sites of Zea mays PHO1, replacing serine residues with alanine, glutamic acid, and aspartic acid, to discern the effects of phosphorylation. Our findings indicate that phosphorylation exerts no impact on the stability or localization of PHO1. Nonetheless, our enzymatic assays unveiled a crucial role for phosphorylation at the S566 residue within the L80 region of the PHO1 structure, suggesting a potential modulation or enhancement of PHO1 activity. These data advance our understanding of starch biosynthesis regulation and present potential targets for crop yield optimization.

Abstract Background Phytohormone abscisic acid (ABA) is involved in the regulation of a wide range of biological processes. In Arabidopsis, it has been well-known that SnRK2s are the central components of the ABA signaling pathway that control the balance between plant growth and stress response, but the functions of ZmSnRK2 in maize are rarely reported. Therefore, the study of ZmSnRK2 is of great importance to understand the ABA signaling pathways in maize. Results In this study, 14 ZmSnRK2 genes were identified in the latest version of maize genome database. Phylogenetic analysis revealed that ZmSnRK2s are divided into three subclasses based on their diversity of C-terminal domains. The exon-intron structures, phylogenetic, synteny and collinearity analysis indicated that SnRK2s, especially the subclass III of SnRK2, are evolutionally conserved in maize, rice and Arabidopsis. Subcellular localization showed that ZmSnRK2 proteins are localized in the nucleus and cytoplasm. The RNA-Seq datasets and qRT-PCR analysis showed that ZmSnRK2 genes exhibit spatial and temporal expression patterns during the growth and development of different maize tissues, and the transcript levels of some ZmSnRK2 genes in kernel are significantly induced by ABA and sucrose treatment. In addition, we found that ZmSnRK2.10, which belongs to subclass III, is highly expressed in kernel and activated by ABA. Overexpression of ZmSnRK2.10 partially rescued the ABA-insensitive phenotype of snrk2.2/2.3 double and snrk2.2/2.3/2.6 triple mutants and led to delaying plant flowering in Arabidopsis. Conclusion The SnRK2 gene family exhibits a high evolutionary conservation and has expanded with whole-genome duplication events in plants. The ZmSnRK2s expanded in maize with whole-genome and segmental duplication, not tandem duplication. The expression pattern analysis of ZmSnRK2s in maize offers important information to study their functions. Study of the functions of ZmSnRK.10 in Arabidopsis suggests that the ABA-dependent members of SnRK2s are evolutionarily conserved in plants. Our study elucidated the structure and evolution of SnRK2 genes in plants and provided a basis for the functional study of ZmSnRK2s protein in maize.

ADP-Glc pyrophosphorylase (AGPase) is a pivotal enzyme catalyzing the conversion of ATP and glucose-1-phosphate (Glc-1-P) to adenosine diphosphate glucose (ADP-Glc), thereby serving as a rate-limiting factor in starch biosynthesis in crops. Although previous investigations have suggested phosphorylation-based regulation of AGPase in maize, the explicit modulation mechanisms have yet to be elucidated. This research evaluated the effect of point mutations at phosphorylation sites (identified using iTRAQTM AB SCIEX, Framingham, MA, USA) on the subcellular localization and activity of the AGPase small subunit Bt2, and its interaction with the large subunit Sh2, in maize. Despite the induction of point mutations, subcellular localization of the Bt2 subunit remained unaltered, primarily within the cytoplasm and nucleus. The interaction between Bt2 and Sh2 subunits continued, mainly in the chloroplast. Notably, an increase in AGPase activity was observed in the case of simulated phosphorylation point mutations, whereas dephosphorylation activity significantly diminished relative to the wild type. These findings demonstrate that point mutations do not affect the subcellular localization of the Bt2 subunit or its interaction with the Sh2 subunit, but substantially modulate AGPase activity. This study provides critical insights into the role of point mutations in enhancing AGPase activity, thus potentially accelerating the production of ADP-Glc, the primary substrate for starch synthesis, promising implications for improved starch biosynthesis in maize.

In order to investigate the crucial role of ZmBT1 in starch accumulation during maize grain development and analyze the expression and distribution of ZmBT1 in various maize tissues, we prepared a polyclonal antibody. Specifically, we successfully expressed the recombinant plasmid pGEX-6p-ZmBT1-C (382-437aa) and purified Gst-ZmBT1-C as the antigen for antibody preparation. Our results confirmed that the ZmBT1 protein in maize tissues can be specifically recognized by the ZmBT1 antibody. Through Western blotting, we observed that the expression protein of ZmBT1 varied by tissues, with the highest content in the grain and endosperm. Furthermore, we employed a combination of Western blotting and quantitative real-time PCR to show that the expression level of ZmBT1 can be influenced by plant hormones. This finding suggests that ZmBT1 plays a critical role in the accumulation of starch and opens up new avenues for functional studies of this protein.

The process of starch biosynthesis is a major developmental event that affects the final grain yield and quality in maize (Zea mays L.), and transcriptional regulation plays a key role in modulating the expression of the main players in the pathway. ZmBt2, which encodes the small subunits of AGPase, is a rate-controlling gene of the pathway; however, much remains unknown about its transcriptional regulation. Our earlier study identifies a short functional fragment of ZmBt2 promoter (394-bp), and further shows it contains multiple putative cis-acting regulatory elements, demonstrating that several transcription factors may govern ZmBt2 expression. Here, we identified a novel TCP transcription factor (TF), ZmTCP7, that interacted with the functional fragment of the ZmBt2 promoter in a yeast one hybrid screening system. We further showed that ZmTCP7 is a non-autonomous TF targeted to the nucleus and predominantly expressed in maize endosperm. Using promoter deletion analyzes by transient expression in maize endosperm protoplasts combined with electrophoretic mobility shift assays, we found that ZmTCP7 bound to GAACCCCAC elements on the ZmBt2 promoter to suppress its expression. Transgenic overexpression of ZmTCP7 in maize caused a significant repression of ZmBt2 transcription by ~77.58%, resulting in a 21.51% decrease in AGPase activity and a 9.58% reduction in the endosperm starch content of transgenic maize. Moreover, the expressions of ZmBt1, ZmSSI, ZmSSIIa, and ZmSSIIIa were increased, while those of ZmSh2 and ZmSSIV reduced significantly in the endosperm of the transgenic maize. Overall, this study shows that ZmTCP7 functions as a transcriptional repressor of ZmBt2 and a negative regulator of endosperm starch accumulation, providing new insights into the regulatory networks that govern ZmBt2 expression and starch biosynthesis pathway in maize.

Maize (Zea mays L.) is one of the most important food crops in the world, and starch is the main component of its endosperm. Transcriptional regulation plays a vital role in starch biosynthesis. However, it is not well understood in maize. We report the identification of the transcription factor ZmNAC126 and its role in regulation of starch synthesis in maize. Transcriptional expression of ZmNAC126 was higher in maize endosperm and kernels than in roots or stems. ZmNAC126 shared a similar expression pattern with starch synthesis genes during seed development, and its expression pattern was also consistent with the accumulation of starch. ZmNAC126 is a typical transcription factor with a transactivation domain between positions 201 and 227 of the amino acid sequence, is located in the nucleus, and binds to CACG repeats in vitro. Yeast one-hybrid assay revealed that ZmNAC126 bound the promoters of ZmGBSSI, ZmSSIIa, ZmSSIV, ZmISA1, and ZmISA2. Transient overexpression of ZmNAC126 in maize endosperm increased the activities of promoters pZmSh2, pZmBt2, pZmGBSSI, pZmSSIIIa, and pZmBT1 but inhibited the activities of pZmISA1 and pZmISA2. ZmNAC126 thus acts in starch synthesis by transcriptionally regulating targeted starch synthesis-related genes in maize kernels.

Abstract Background Endosperm-trait related genes are associated with grain yield or quality in maize. There are vast numbers of these genes whose functions and regulations are still unknown. The biolistic system, which is often used for transient gene expression, is expensive and involves complex protocol. Besides, it cannot be used for simultaneous analysis of multiple genes. Moreover, the biolistic system has little physiological relevance when compared to cell-specific based system. Plant protoplasts are efficient cell-based systems which allow quick and simultaneous transient analysis of multiple genes. Typically, PEG-calcium mediated transfection of protoplast is simple and cost-effective. Notably, starch granules in cereal endosperm may diminish protoplast yield and integrity, if the isolation and transfection conditions are not accurately measured. Prior to this study, no PEG-calcium mediated endosperm protoplast system has been reported for cereal crop, perhaps, because endosperm cells accumulate starch grains. Results Here, we showed the uniqueness of maize endosperm-protoplast system (EPS) in conducting endosperm cell-based experiments. By using response surface designs, we established optimized conditions for the isolation and PEG-calcium mediated transfection of maize endosperm protoplasts. The optimized conditions of 1% cellulase, 0.75% macerozyme and 0.4 M mannitol enzymolysis solution for 6 h showed that more than 80% protoplasts remained viable after re-suspension in 1 ml MMG. The EPS was used to express GFP protein, analyze the subcellular location of ZmBT1, characterize the interaction of O2 and PBF1 by bimolecular fluorescent complementation (BiFC), and simultaneously analyze the regulation of ZmBt1 expression by ZmMYB14. Conclusions The described optimized conditions proved efficient for reasonable yield of viable protoplasts from maize endosperm, and utility of the protoplast in rapid analysis of endosperm-trait related genes. The development of the optimized protoplast isolation and transfection conditions, allow the exploitation of the functional advantages of protoplast system over biolistic system in conducting endosperm-based studies (particularly, in transient analysis of genes and gene regulation networks, associated with the accumulation of endosperm storage products). Such analyses will be invaluable in characterizing endosperm-trait related genes whose functions have not been identified. Thus, the EPS will benefit the research of cereal grain yield and quality improvement.

Abstract Background Short internodes contribute to plant dwarfism, which is exceedingly beneficial for crop production. However, the underlying mechanisms of internode elongation are complicated and have been not fully understood. Results Here, we report a maize dwarf mutant, dwarf2014 (d2014), which displays shortened lower internodes. Map-based cloning revealed that the d2014 gene is a novel br2 allele with a splicing variation, resulting in a higher expression of BR2-T02 instead of normal BR2-T01. Then, we found that the internode elongation in d2014/br2 exhibited a pattern of inhibition-normality-inhibition (transient for the ear-internode), correspondingly, at the 6-leaf, 12-leaf and 14-leaf stages. Indeed, BR2 encodes a P-glycoprotein1 (PGP1) protein that functions in auxin efflux, and our in situ hybridization assay showed that BR2 was mainly expressed in vascular bundles of the node and internode. Furthermore, significantly higher auxin concentration was detected in the stem apex of d2014 at the 6-leaf stage and strictly in the node region for the ear-internode at the 14-leaf stage. In such context, we propose that BR2/PGP1 transports auxin from node to internode through the vascular bundles, and excessive auxin accumulation in the node (immediately next to the intercalary meristem) region suppresses internode elongation of d2014. Conclusions These findings suggest that low auxin levels mediated by BR2/PGP1 in the intercalary meristem region are crucial for internode elongation.

Abstract Background Utilization of heterosis in maize could be critical in maize breeding for boosting grain yield. However, the genetic architecture of heterosis is not fully understood. To dissect the genetic basis of yield-related traits and heterosis in maize, 301 recombinant inbred lines derived from 08 to 641 × YE478 and 298 hybrids from the immortalized F2 (IF2) population were used to map quantitative trait loci (QTLs) for nine yield-related traits and mid-parent heterosis. Results We observed 156 QTLs, 28 pairs of loci with epistatic interaction, and 10 significant QTL × environment interactions in the inbred and hybrid mapping populations. The high heterosis in F1 and IF2 populations for kernel weight per ear (KWPE), ear weight per ear (EWPE), and kernel number per row (KNPR) matched the high percentages of QTLs (over 50%) for those traits exhibiting overdominance, whereas a notable predominance of loci with dominance effects (more than 70%) was observed for traits that show low heterosis such as cob weight per ear (CWPE), rate of kernel production (RKP), ear length (EL), ear diameter (ED), cob diameter, and row number (RN). The environmentally stable QTL qRKP3–2 was identified across two mapping populations, while qKWPE9, affecting the trait mean and the mid-parent heterosis (MPH) level, explained over 18% of phenotypic variations. Nine QTLs, qEWPE9–1, qEWPE10–1, qCWPE6, qEL8, qED2–2, qRN10–1, qKWPE9, qKWPE10–1, and qRKP4–3, accounted for over 10% of phenotypic variation. In addition, QTL mapping identified 95 QTLs that were gathered together and integrated into 33 QTL clusters on 10 chromosomes. Conclusions The results revealed that (1) the inheritance of yield-related traits and MPH in the heterotic pattern improved Reid (PA) × Tem-tropic I (PB) is trait-dependent; (2) a large proportion of loci showed dominance effects, whereas overdominance also contributed to MPH for KNPR, EWPE, and KWPE; (3) marker-assisted selection for markers at genomic regions 1.09–1.11, 2.04, 3.08–3.09, and 10.04–10.05 contributed to hybrid performance per se and heterosis and were repeatedly reported in previous studies using different heterotic patterns is recommended.

Starch biosynthesis is a complex and highly controlled process that requires coordinated activities among multiple enzymes. Starch synthase III (SSIII) is the largest protein in the starch synthase complex and its function is to lengthen long-chain amylopectin in starch synthesis. It potentially affects the activity of other key enzymes in starch synthesis through protein–protein interactions; therefore, its function and regulation play a predominant role in starch synthesis. In this review, we summarized the main research of SSIII including its biochemical characteristics, structural features, expression atlas, and regulation means. Structural features and expressional analysis indicated that SSIIIa is the main functional protein in maize endosperm rather than SSIIIb-a and SSIIIb-b, even though they are similar in the tertiary structures. The regulation investigation of SSIIIa showed that there are 13 transcription factors that control the transcription of SSIIIa. Interaction network analysis showed that SSIIIa could be involved with ten other key enzymes in starch synthesis. In conclusion, this review considerably extends our understanding of SSIII and provides the theoretical basis for improving starch synthesis by SSIII in maize.

Proper development of the maize kernel is of great significance for high and stable maize yield to ensure national food security. Gibberellin (GA), one of the hormones regulating plant growth, is involved in modulating the development of maize kernels. Cellulose, one of the main components of plant cells, is also regulated by gibberellin. The mechanism of hormone regulation during maize grain development is highly complicated, and reports on GA-mediated modulation of cellulose synthesis during maize grain development are rare. Our study revealed that during grain growth and development, the grain length and bulk density of GA-treated corn kernels improved significantly, and the cellulose content of grains increased, while seed coat thickness decreased. The transcription factor basic region/leucine zipper motif 53 (bZIP53), which is strongly correlated with cellulose synthase gene 1 (CesA1) expression, was screened by transcriptome sequencing and the expression of the cellulose synthase gene in maize grain development after GA treatment was determined. It was found that bZIP53 expression significantly promoted the expression of CesA1. Further, analysis of the transcription factor bZIP53 determined that the gene-encoded protein was localized in the cell and nuclear membranes, but the transcription factor bZIP53 itself showed no transcriptional activation. Further studies are required to explore the interaction of bZIP53 with CesA1.

Abstract When detecting the objects in videos, motion always leads to object deterioration, like blurring and occlusion, as well as the strange state of the object's shape and posture. Consequently, the detection of video frames will lead to a decline in accuracy by using the image object detection model. This paper proposes an online video object detection method based on the one‐stage detector YOLOx. First, the module for space–time feature aggregation is given, which uses the space–time information of past frames to enhance the feature quality of the current frame. Then, the module for result reuse is given, which incorporates the detection results of past frames to improve the detection stability of the current frame. By these two modules, the trade‐off between accuracy and speed of video object detection could be achieved. Experimental results on the ImageNet VID show the improvement of speed and accuracy of the proposed method.

Abstract Image denoising aims to remove noise from images and improve the quality of images. However, most image denoising methods heavily rely on pairwise training strategies and strict prior knowledge about image structure or noise distribution. While these methods exhibit significant results when handling known types of noise, their generalization performance diminishes when confronted with images containing unknown noise distributions. To address this issue, a two‐stage approach is introduced for enhancing the generalizability of image denoising. The proposed method does not rely on a large amount of paired data or prior knowledge of the noise type and level. Instead, it constructs a denoising pipeline with improved generalizability through an MLP‐based denoiser and generative diffusion prior. Specifically, in the first stage, an initial denoised image is predicted with a structure resembling that of the underlying clean image by introducing an MLP‐based U‐shaped denoising network aided by an implicit structural prior. In the second stage, the generalizability and quality of the denoiser are further enhanced by conditioning the result obtained from the previous stage on the pretrained denoising diffusion null‐space model. Extensive experimentation on multiple datasets demonstrates that this method exhibits better denoising performance and generalizability than other image denoising methods.

Insulators are crucial insulation components and structural supports in power grids, playing a vital role in the transmission lines. Due to temperature fluctuations, internal stress, or damage from hail, insulators are prone to injury. Automatic detection of damaged insulators faces challenges such as diverse types, small defect targets, and complex backgrounds and shapes. Most research for detecting insulator defects has focused on a single defect type or a specific material. However, the insulators in the grid's transmission lines have different colors and materials. Various insulator defects coexist, and the existing methods have difficulty meeting the practical application requirements. Current methods suffer from low detection accuracy and mAP0.5 cannot meet application requirements. This paper proposes an improved you only look once version 7 (YOLOv7) model for multi-type insulator defect detection. First, our model replaces the spatial pyramid pooling cross stage partial network (SPPCSPC) module with the receptive filed block (RFB) module to enhance the network's feature extraction capability. Second, a coordinate attention (CA) mechanism is introduced into the head part to enhance the network's feature representation ability and to improve detection accuracy. Third, a wise intersection over union (WIoU) loss function is employed to address the low-quality samples hindering model generalization during training, thereby improving the model's overall performance. The experimental results indicate that the proposed model exhibits enhancements across various performance metrics. Specifically, there is a 1.6% advancement in mAP_0.5, a corresponding 1.6% enhancement in mAP_0.5:0.95, a 1.3% elevation in precision, and a 1% increase in recall. Moreover, the model achieves parameter reduction by 3.2 million, leading to a decrease of 2.5 GFLOPS in computational cost. Notably, there is also an improvement of 2.81 milliseconds in single-image detection speed. This improved model can detect insulator defects for diverse materials, color insulators, and partial damage shapes in complex backgrounds.

The process of starch biosynthesis is a major developmental event that affects the final grain yield and quality in maize (Zea mays L.), and transcriptional regulation plays a key role in modulating the expression of the main players in the pathway. ZmBt2, which encodes the small subunits of AGPase, is a rate-controlling gene of the pathway; however, much remains unknown about its transcriptional regulation. Our earlier study identifies a short functional fragment of ZmBt2 promoter (394-bp), and further shows it contains multiple putative cis-acting regulatory elements, demonstrating that several transcription factors may govern ZmBt2 expression. Here, we identified a novel TCP transcription factor (TF), ZmTCP7, that interacted with the functional fragment of the ZmBt2 promoter in a yeast one hybrid screening system. We further showed that ZmTCP7 is a non-autonomous TF targeted to the nucleus and predominantly expressed in maize endosperm. Using promoter deletion analyzes by transient expression in maize endosperm protoplasts combined with electrophoretic mobility shift assays, we found that ZmTCP7 bound to GAACCCCAC elements on the ZmBt2 promoter to suppress its expression. Transgenic overexpression of ZmTCP7 in maize caused a significant repression of ZmBt2 transcription by ~77.58%, resulting in a 21.51% decrease in AGPase activity and a 9.58% reduction in the endosperm starch content of transgenic maize. Moreover, the expressions of ZmBt1, ZmSSI, ZmSSIIa, and ZmSSIIIa were increased, while those of ZmSh2 and ZmSSIV reduced significantly in the endosperm of the transgenic maize. Overall, this study shows that ZmTCP7 functions as a transcriptional repressor of ZmBt2 and a negative regulator of endosperm starch accumulation, providing new insights into the regulatory networks that govern ZmBt2 expression and starch biosynthesis pathway in maize. [ABSTRACT FROM AUTHOR]

The process of starch biosynthesis is a major developmental event that affects the final grain yield and quality in maize (Zea mays L.), and transcriptional regulation plays a key role in modulating the expression of the main players in the pathway. ZmBt2, which encodes the small subunits of AGPase, is a rate-controlling gene of the pathway; however, much remains unknown about its transcriptional regulation. Our earlier study identifies a short functional fragment of ZmBt2 promoter (394- bp), and further shows it contains multiple putative cis-acting regulatory elements, demonstrating that several transcription factors may govern ZmBt2 expression. Here, we identified a novel TCP transcription factor (TF), ZmTCP7, that interacted with the functional fragment of the ZmBt2 promoter in a yeast one hybrid screening system. We further showed that ZmTCP7 is a non-autonomous TF targeted to the nucleus and predominantly expressed in maize endosperm. Using promoter deletion analyzes by transient expression in maize endosperm protoplasts combined with electrophoretic mobility shift assays, we found that ZmTCP7 bound to GAACCCCAC elements on the ZmBt2 promoter to suppress its expression. Transgenic overexpression of ZmTCP7 in maize caused a significant repression of ZmBt2 transcription by 77.58%, resulting in a 21.51% decrease in AGPase activity and a 9.58% reduction in the endosperm starch content of transgenic maize. Moreover, the expressions of ZmBt1, ZmSSI, ZmSSIIa, and ZmSSIIIa were increased, while those of ZmSh2 and ZmSSIV reduced significantly in the endosperm of the transgenic maize. Overall, this study shows that ZmTCP7 functions as a transcriptional repressor of ZmBt2 and a negative regulator of endosperm starch accumulation, providing new insights into the regulatory networks that govern ZmBt2 expression and starch biosynthesis pathway in maize. [ABSTRACT FROM AUTHOR]

Meteorological disasters along highways significantly reduce road traffic efficiency. Low visibility caused by heavy fog is a severe meteorological disaster that greatly increases highway traffic accidents. Accurately predicting highway visibility and taking timely response measures can reduce the impact of meteorological disasters and improve traffic safety. We proposed an Attention-based BiLSTM-CNN (ABCNet) model, which synergized attention mechanisms with BiLSTM and CNN technologies to forecast atmospheric visibility more accurately. First, the Bi-LSTM module processed information both forward and backward, capturing intricate temporal dependencies in the model. Second, the multi-head attention mechanism following the Bi-LSTM distilled and prioritized salient features from multiple aspects of the sequence data. Third, the CNN module recognized local spatial features, and a singular attention mechanism refined the feature map after the CNN module, further enhancing the model's accuracy and predictive capability. Experiments showed that the model was accurate, effective, and significantly advanced compared to conventional models. It could fully extract the spatiotemporal characteristics of meteorological elements. The model was integrated into practical systems with positive results. Additionally, this study provides a self-collected meteorological dataset for highways in high-altitude mountainous areas.

Fire incidents near power transmission lines pose significant safety hazards to the regular operation of the power system. Therefore, achieving fast and accurate smoke detection around power transmission lines is crucial. Due to the complexity and variability of smoke scenarios, existing smoke detection models suffer from low detection accuracy and slow detection speed. This paper proposes an improved model for smoke detection in high-voltage power transmission lines based on the improved YOLOv7-tiny. First, we construct a dataset for smoke detection in high-voltage power transmission lines. Due to the limited number of real samples, we employ a particle system to randomly generate smoke and composite it into randomly selected real scenes, effectively expanding the dataset with high quality. Next, we introduce multiple parameter-free attention modules into the YOLOv7-tiny model and replace regular convolutions in the Neck of the model with Spd-Conv (Space-to-depth Conv) to improve detection accuracy and speed. Finally, we utilize the synthesized smoke dataset as the source domain for model transfer learning. We pre-train the improved model and fine-tune it on a dataset consisting of real scenarios. Experimental results demonstrate that the proposed improved YOLOv7-tiny model achieves a 2.61% increase in mean Average Precision (mAP) for smoke detection on power transmission lines compared to the original model. The precision is improved by 2.26%, and the recall is improved by 7.25%. Compared to other object detection models, the smoke detection proposed in this paper achieves high detection accuracy and speed. Our model also improved detection accuracy on the already publicly available wildfire smoke dataset Figlib (Fire Ignition Library).

In tobacco production, cigarettes with appearance defects are inevitable and dramatically impact the quality of tobacco products. Currently, available methods do not balance the tension between detection accuracy and speed. To achieve accurate detection on a cigarette production line with the rate of 200 cigarettes per second, we propose a defect detection model for cigarette appearance based on YOLOv5n (You Only Look Once Version 5 Nano), called CJS-YOLOv5n (YOLOv5n with C2F (Cross Stage Partial (CSP) Bottleneck with 2 convolutions-fast), Jump Concat, and SCYLLA-IoU (SIoU)). This model incorporates the C2F module proposed in the state-of-the-art object detection network YOLOv8 (You Only Look Once Version 8). This module optimizes the network by parallelizing additional gradient flow branches, enhancing the model's feature extraction capability and obtaining richer gradient information. Furthermore, this model uses Jump Concat to preserve minor defect feature information during the fusion process in the feature fusion pyramid's P4 layer. Additionally, this model integrates the SIoU localization loss function to improve localization accuracy and detection precision. Experimental results demonstrate that our proposed CJS-YOLOv5n model achieves superior overall performance. It maintains a detection speed of over 500 FPS (frames per second) while increasing the recall rate by 2.3% and mAP (mean average precision)@0.5 by 1.7%. The proposed model is suitable for application in high-speed cigarette production lines.