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Phosphorus (P) is a crucial macronutrient for plant growth and development, and low-Pi stress poses a significant limitation to maize production. While the role of the SPX domain in encoding proteins involved in phosphate (Pi) homeostasis and signaling transduction has been extensively studied in other model plants, the molecular and functional characteristics of the SPX gene family members in maize remain largely unexplored. In this study, we identified six SPX members, and the phylogenetic analysis of ZmSPXs revealed a close relationship with SPX genes in rice. The promoter regions of ZmSPXs were abundant in biotic and abiotic stress-related elements, particularly associated with various hormone signaling pathways, indicating potential intersections between Pi signaling and hormone signaling pathways. Additionally, ZmSPXs displayed tissue-specific expression patterns, with significant and differential induction in anthers and roots, and were localized to the nucleus and cytoplasm. The interaction between ZmSPXs and ZmPHRs was established via yeast two-hybrid assays. Furthermore, overexpression of ZmSPX1 enhanced root sensitivity to Pi deficiency and high-Pi conditions in Arabidopsis thaliana. Phenotypic identification of the maize transgenic lines demonstrated the negative regulatory effect on the P concentration of stems and leaves as well as yield. Notably, polymorphic sites including 34 single-nucleotide polymorphisms (SNPs) and seven insertions/deletions (InDels) in ZmSPX1 were significantly associated with 16 traits of low-Pi tolerance index. Furthermore, significant sites were classified into five haplotypes, and haplotype5 can enhance biomass production by promoting root development. Taken together, our results suggested that ZmSPX family members possibly play a pivotal role in Pi stress signaling in plants by interacting with ZmPHRs. Significantly, ZmSPX1 was involved in the Pi-deficiency response verified in transgenic Arabidopsis and can affect the Pi concentration of maize tissues and yield. This work lays the groundwork for deeper exploration of the maize SPX family and could inform the development of maize varieties with improved Pi efficiency.

Abstract The urban street is a congested environment that contains a large number of occluded and size-differentiated objects. Aiming at the problems of the loss of the target to be detected and low detection accuracy resulting from this situation, a newly improved algorithm, based on YOLOv4, DCYOLO is proposed. Firstly, a Difference sensitive network (DSN) is introduced to extract the edge features of objects from the original image. Then, assign the edge features back to increase the edge intensity of the object in the original image and ultimately improve the detection performance. Secondly, the feature fusion module (CFFB) based on context information is introduced to realize the cross-scale fusion of shallow fine-grained features and deep-level features, to strengthen the cross-scale semantic information fusion of feature maps and eventually improve the performance of object detection. At last, in the network prediction part, the SIOU loss function replaces the original CIOU loss function to improve the convergence speed and accuracy of object detection. The experiments based on MS COCO 2017 and self-made datasets show that, compared with the YOLOv4, the detection accuracy of DCYOLO models is greatly improved with an increase of 9.1 percentage points in AP and 10.4 percentage points in APs. Compared with YOLOv5x and Faster R-CNN, DCYOLO shows higher accuracy and better detection performance. The experiment result proves that the DCYOLO algorithm can adapt to the dense object detection requirements in the congested environment of urban streets.

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.

Abstract With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human‐computer interaction more natural and flexible, bringing richer interactive experience to teaching, on‐board control, electronic games, etc. In order to perform robust recognition under the conditions of illumination change, background clutter, rapid movement, partial occlusion, an algorithm based on multi‐level feature fusion of two‐stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains RGB‐D images to establish a gesture database. At the same time, data enhancement is performed on training and test sets. Then, a model of multi‐level feature fusion of two‐stream convolutional neural network is established and trained. Experiments result show that the proposed network model can robustly track and recognize gestures, and compared with the single‐channel model, the average detection accuracy is improved by 1.08%, and mean average precision (mAP) is improved by 3.56%. The average recognition rate of gestures under occlusion and different light intensity was 93.98%. Finally, in the ASL dataset, LaRED dataset, and 1‐miohand dataset, recognition accuracy shows satisfactory performances compared to the other method.

Within the realm of dental material innovation, this study pioneers the incorporation of tung oil into polyurea coatings, setting a new precedent for enhancing self-healing functionality and durability. Originating from an ancient practice, tung oil is distinguished by its outstanding water resistance and microbial barrier efficacy. By synergizing it with polyurea, we developed coatings that unite mechanical strength with biological compatibility. The study notably quantifies self-healing efficiency, highlighting the coatings’ exceptional capacity to mend physical damages and thwart microbial incursions. Findings confirm that tung oil markedly enhances the self-repair capabilities of polyurea, leading to improved wear resistance and the inhibition of microbial growth, particularly against Streptococcus mutans, a principal dental caries pathogen. These advancements not only signify a leap forward in dental material science but also suggest a potential redefinition of dental restorative practices aimed at prolonging the lifespan of restorations and optimizing patient outcomes. Although this study lays a substantial foundation for the utilization of natural oils in the development of medical-grade materials, it also identifies the critical need for comprehensive cytotoxicity assays. Such evaluations are essential to thoroughly assess the biocompatibility and the safety profile of these innovative materials for clinical application. Future research will concentrate on this aspect, ensuring that the safety and efficacy of the materials align with clinical expectations for dental restorations.

The study of yield traits can reveal the genetic architecture of grain yield for improving maize production. In this study, an association panel comprising 362 inbred lines and a recombinant inbred line population derived from X178 × 9782 were used to identify candidate genes for nine yield traits. High-priority overlap (HPO) genes, which are genes prioritized in a genome-wide association study (GWAS), were investigated using coexpression networks. The GWAS identified 51 environmentally stable SNPs in two environments and 36 pleiotropic SNPs, including three SNPs with both attributes. Seven hotspots containing 41 trait-associated SNPs were identified on six chromosomes by permutation. Pyramiding of superior alleles showed a highly positive effect on all traits, and the phenotypic values of ear diameter and ear weight consistently corresponded with the number of superior alleles in tropical and temperate germplasm. A total of 61 HPO genes were detected after trait-associated SNPs were combined with the coexpression networks. Linkage mapping identified 16 environmentally stable and 16 pleiotropic QTL. Seven SNPs that were located in QTL intervals were assigned as consensus SNPs for the yield traits. Among the candidate genes predicted by our study, some genes were confirmed to function in seed development. The gene Zm00001d016656 encoding a serine/threonine protein kinase was associated with five different traits across multiple environments. Some genes were uniquely expressed in specific tissues and at certain stages of seed development. These findings will provide genetic information and resources for molecular breeding of maize grain yield.

In the context of increasing uncertainty in the international economic environment and changes in the labor market, it is imperative to pay proper attention to the environmental quality and sustainability. This paper theoretically analyzes the impact of financial development and financial inclusion on the environmental sustainability. Based on the survey data of 2093 households in Xunyi and Yangling, Shaanxi Province, China, the 2SLS model, and the instrumental variable quantile regression model are constructed to test the association of variables. The results show that the development of financial inclusion and financial development have an inverted “U” shaped nonlinear effect on the environmental quality and efficiency; Only after the development of financial inclusion reaches a certain degree can the environmental quality and sustainability be effectively improved. The results of the quantile regression of instrumental variables show that financial inclusion significantly improves the environmental quality with medium and high degrees but does not have a significant effect on the environmental quality and sustainability with low degrees. After considering the endogeneity and robustness test, the above conclusion still holds. Further research shows that inclusive financial development improves environmental quality and results in sustainable development. Improving the convenience and depth of use of financial services is the key to effectively reducing rural household economic vulnerability and improve the environmental quality. The heterogeneity analysis shows that financial inclusion development has a stronger effect on improving the environmental quality. This study provides empirical evidence and policy implications to better promote financial inclusion to improve the economic vulnerability of rural households, improve environmental quality and achieve the sustainable development. These findings provide policy support to better promote financial inclusion to improve the economic vulnerability of rural households, improve environmental quality and achieve the sustainable development, which can ameliorate environmental degradation, and create a safe, healthy and sustainable environment for achieving sustainable development goals.

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.

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.

Abstract Background Long noncoding RNAs (lncRNAs) play important roles in essential biological processes. However, our understanding of lncRNAs as competing endogenous RNAs (ceRNAs) and their responses to nitrogen stress is still limited. Results Here, we surveyed the lncRNAs and miRNAs in maize inbred line P178 leaves and roots at the seedling stage under high-nitrogen (HN) and low-nitrogen (LN) conditions using lncRNA-Seq and small RNA-Seq. A total of 894 differentially expressed lncRNAs and 38 different miRNAs were identified. Co-expression analysis found that two lncRNAs and four lncRNA-targets could competitively combine with ZmmiR159 and ZmmiR164, respectively. To dissect the genetic regulatory by which lncRNAs might enable adaptation to limited nitrogen availability, an association mapping panel containing a high-density single–nucleotide polymorphism (SNP) array (56,110 SNPs) combined with variable LN tolerant-related phenotypes obtained from hydroponics was used for a genome-wide association study (GWAS). By combining GWAS and RNA-Seq, 170 differently expressed lncRNAs within the range of significant markers were screened. Moreover, 40 consistently LN-responsive genes including those involved in glutamine biosynthesis and nitrogen acquisition in root were identified. Transient expression assays in Nicotiana benthamiana demonstrated that LNC_002923 could inhabit ZmmiR159-guided cleavage of Zm00001d015521. Conclusions These lncRNAs containing trait-associated significant SNPs could consider to be related to root development and nutrient utilization. Taken together, the results of our study can provide new insights into the potential regulatory roles of lncRNAs in response to LN stress, and give valuable information for further screening of candidates as well as the improvement of maize resistance to LN stress.

With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human–computer interaction more natural and flexible, bringing the richer interactive experience to teaching, on‐board control, electronic games etc. To perform robust recognition under the conditions of illumination change, background clutter, rapid movement, and partial occlusion, an algorithm based on multi‐level feature fusion of two‐stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains red–green–blue‐depth (RGB‐D) images to establish a gesture database. At the same time, data enhancement is performed on the training set and test set. Then, a model of multi‐level feature fusion of a two‐stream convolutional neural network is established and trained. Experiments show that the proposed network model can robustly track and recognise gestures under complex backgrounds (such as similar complexion, illumination changes, and occlusion), and compared with the single‐channel model, the average detection accuracy is improved by 1.08%, and mean average precision is improved by 3.56%.

This paper studies the electromagnetic properties of defected ground structure (DGS) with von Koch snowflake fractals. First, the snowflake fractal is employed in the dumbbell shaped DGS, and we have discovered that it has the characteristics of a low-pass filter and the out-of-band rejection increases with fractal iterations. In consideration of fabrication accuracy and performance, the third iteration has been adopted. Based on this structure, we have designed a narrow-band filter and a wide-band filter to verify our theory. From the simulated and measured results, we can see that the out-of-band rejection level has been greatly improved. The proposed structure can be applied in communication planar circuits.

At present, urban computing and intelligence has become an important topic in the research field of artificial intelligence. On the other hand, computer vision as a crucial bridge between urban world and artificial intelligence is playing a key role in urban computing and intelligence. Conventional particle filter is derived from Karman filter, which theoretically based on Monte Carlo method. Sequential importance resampling (SIR) is implemented in conventional particle filter to avoid the degeneracy problem. In order to overcome the shortcomings of the resampling algorithm in the traditional particle filter, we proposed an optimized particle filter using the maximum variance weight segmentation resampling algorithm in this paper, which improved the performance of particle filter. Compared with the traditional particle filter algorithm, the experimental results show that the proposed scheme outperforms in terms of computational consumption and the accuracy of particle tracking. The final experimental results proved that the quality of the maximum variance weight segmentation method increased the accuracy and stability in motion trajectory tracking tasks.

Phosphorus (P) is an essential macroelement supporting maize productivity and low-P stress is a limiting factor of maize growth and yield. Improving maize plant tolerance to low P through molecular breeding is an effective alternative to increase crop productivity. In this study, a total of 111 diverse maize inbred lines were used to identify the favorable alleles and nucleotide diversity of candidate ZmNAC9, which plays an important role in response to low P and regulation in root architecture. A significant difference was found under low- and sufficient-P conditions for each of the 22 seedling traits, and a total of 41 polymorphic sites including 32 single nucleotide polymorphisms (SNPs) and 9 insertion and deletions (InDels) were detected in ZmNAC9 among 111 inbred lines. Among the 41 polymorphic studied sites, a total of 39 polymorphic sites were associated with 20 traits except for the dry weight of shoots and forks, of which six sites were highly significantly associated with a diverse number of low-P tolerant root trait index values by using a mixed linear model (MLM) at −log10 P = 3.61. In addition, 29 polymorphic sites under P-sufficient and 32 polymorphic sites under P-deficient conditions were significantly associated with a diverse number of seedling traits, of which five polymorphic sites (position S327, S513, S514, S520, and S827) were strongly significantly associated with multiple seedling traits under low-P and normal-P conditions. Among highly significant sites, most of the sites were associated with root traits under low-P, normal-P, and low-P trait index values. Linkage disequilibrium (LD) was strong at (r2 > 1.0) in 111 inbred lines. Furthermore, the effect of five significant sites was verified for haplotypes in 111 lines and the favorable allele S520 showed a positive effect on the dry weight of roots under the low-P condition. Furthermore, the expression pattern confirmed that ZmNAC9 was highly induced by low P in the roots of the P-tolerant 178 inbred line. Moreover, the subcellular localization of ZmNAC9 encoded by protein was located in the cytoplasm and nucleus. Haplotypes carrying more favorable alleles exhibited superior effects on phenotypic variation and could be helpful in developing molecular markers in maize molecular breeding programs. Taken together, the finding of this study might lead to further functions of ZmNAC9 and genes that might be involved in responses to low-P stress in maize.

We theoretically and experimentally demonstrate a scheme to implement bipolar millimeter-wave (MMW) ultrawideband (UWB) monocycles using a nonlinear optical loop mirror (NOLM). A 20-GHz optical clock as the probe light and a Gaussian pulse as the pump light are launched into the NOLM for cross-phase modulation (XPM). At the reflection and transmission ports of the NOLM, a pair of polarity-reversed MMW-Gaussian pulses is generated due to constructive and destructive interferences. They are then mixed with proper time delay and orthogonal polarization states. Thus, a photonic MMW-UWB monocycle is obtained. By adjusting the time delay between the reflection and transmission ports of the NOLM, bipolar MMW-UWB pulses are generated.

We experimentally demonstrate an arbitrary-order bandwidth-tunable differentiator using a programmable optical pulse shaper, which is based on spatial diffraction and liquid crystal modulation. The impact of bandwidth of optical pulse shaper on output power and pulsewidth of differentiation is theoretically analyzed. In the experiment, all first-order, second-order, and third-order differentiators with bandwidths of 80, 160, and 320 GHz are obtained with average deviations of less than 4.2%. The differentiation of pseudorandom nonreturn-to-zero (NRZ) signals at 10 and 20 Gbit/s is also discussed.