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Ship detection is an important but challenging task in the field of computer vision, partially due to the minuscule ship objects in optical remote sensing images and the interference of clouds occlusion and strong waves. Most of the current ship detection methods focus on boosting detection accuracy while they may ignore the detection speed. However, it is also indispensable to increase ship detection speed because it can provide timely ocean rescue and maritime surveillance. To solve the above problems, we propose an improved YOLOv3 (ImYOLOv3) based on attention mechanism, aiming to achieve the best trade-off between detection accuracy and speed. First, to realize high-efficiency ship detection, we adopt the off-the-shelf YOLOv3 as our basic detection framework due to its fast speed. Second, to boost the performance of original YOLOv3 for small ships, we design a novel and lightweight dilated attention module (DAM) to extract discriminative features for ship targets, which can be easily embedded into the basic YOLOv3. The integrated attention mechanism can help our model learn to suppress irrelevant regions while highlighting salient features useful for ship detection task. Furthermore, we introduce a multi-class ship dataset (MSD) and explicitly set supervised subclass according to the scales and moving states of ships. Extensive experiments verify the effectiveness and robustness of ImYOLOv3, and show that our method can accurately detect ships with different scales in different backgrounds, while at a real-time speed.

Crop yield is of a quantitative nature. Two complementary approaches linkage mapping and genome-wide association study (GWAS) have been employed for the identification of loci influencing maize yield. In previous research, we meta-analyzed QTL for maize yield. Here, we tried to integrate meta-QTL and GWAS analyses to dissect candidate genes for maize yield. A total of seven candidate genes were detected by both meta-QTL and two independent GWAS analyses. Functional annotation indicated that some candidate genes were responsible for the maintenance and differentiation of maize inflorescence meristem that directly affect yield productivity. In addition, well-characterized genes for maize yield-related traits, including SBP-box family member unbranched3 (ub3) for maize ear row number, zea floricaula/leafy1 (zfl1) for maize inflorescence architecture and reproductive transition were detected by the integrated meta-QTL and GWAS analyses. Expression analysis showed that some candidate genes were preferentially expressed in maize rapidly proliferated tissues, including shoot apex, tassel, and ear primordia, which influence maize inflorescence architecture and yield performance. Some candidate genes were hypothesized to be selected during maize domestication and improvement. Results presented here will not only facilitate the cloning of yield-related genes, and provide guidance in breeding of plants with high-yield productivity.

The tetracyclic diterpenoid carboxylic acids, gibberellins (GAs), orchestrate a broad spectrum of biological programs. In nature, GAs or GA-like substance is produced in bacteria, fungi, and plants. The function of GAs in microorganisms remains largely unknown. Phytohormones GAs mediate diverse growth and developmental processes through the life cycle of plants. The GA biosynthetic and metabolic pathways in bacteria, fungi, and plants are remarkably divergent. In vascular plants, phytohormone GA, receptor GID1, and repressor DELLA shape the GA–GID1–DELLA module in GA signaling cascade. Sequence reshuffling, functional divergence, and adaptive selection are main driving forces during the evolution of GA pathway components. The GA–GID1–DELLA complex interacts with second messengers and other plant hormones to integrate environmental and endogenous cues, which is beneficial to phytohormones homeostasis and other biological events. In this review, we first briefly describe GA metabolism pathway, signaling perception, and its second messengers. Then, we examine the evolution of GA pathway genes. Finally, we focus on reviewing the crosstalk between GA–GID1–DELLA module and phytohormones. Deciphering mechanisms underlying plant hormonal interactions are not only beneficial to addressing basic biological questions, but also have practical implications for developing crops with ideotypes to meet the future demand.

Based on the connectivity and energy consumption problems in wireless sensor networks, this paper proposes a kind of new network algorithm called the connectivity and energy efficiency (CEE) algorithm to guarantee the connectivity and connectivity probability, and also to reduce the network energy consumption as much as possible. Under the premise that all sensors can communicate with each other in a specific communication radius, we obtained the relationship among the connectivity, the number of sensor nodes, and the communication radius because of the theory of probability and statistics. The innovation of the paper is to maximize the network connectivity and connectivity probability, by choosing which types of sleeping nodes to wake up. According to the node’s residual energy and the relative value of distance, the algorithm reduces the energy consumption of the whole network as much as possible, and wakes up the number of neighbor nodes as little as possible, to improve the service life of the whole network. Simulation results show that this algorithm combines the connectivity and the energy efficiency, provides a useful reference value for the normal operation of the sensors networks.

MAIN CONCLUSION : The meta-QTL and candidate genes will facilitate the elucidation of molecular bases underlying agriculturally important traits and open new avenues for functional markers development and elite alleles introgression in maize breeding program. A large number of QTLs attributed to grain productivity and other agriculturally important traits have been identified and deposited in public repositories. The integration of fruitful QTL becomes a major issue in current plant genomics. To this end, we first collected QTL for six agriculturally important traits in maize, including yield, plant height, ear height, leaf angle, stay-green, and maize rough dwarf disease resistance. The meta-analysis method was then employed to retrieve 113 meta-QTL. Additionally, we also isolated candidate genes for target traits by the bioinformatic technique. Several candidates, including some well-characterized genes, GA3ox2 for plant height, lg1 and lg4 for leaf angle, zfl1 and zfl2 for flowering time, were co-localized with established meta-QTL intervals. Intriguingly, in a relatively narrow meta-QTL region, the maize ortholog of rice yield-related gene GW8/OsSPL16 was believed to be a candidate for yield. Leveraging results presented in this study will provide further insights into the genetic architecture of maize agronomic traits. Moreover, the meta-QTL and candidate genes reported here could be harnessed for the enhancement of stress tolerance and yield performance in maize and translation to other crops.

Automatic ship detection in optical remote sensing images is of great significance due to its broad applications in maritime security and fishery control. Most ship detection algorithms utilize a single-band image to design low-level and hand-crafted features, which are easily influenced by interference like clouds and strong waves and not robust for large-scale variation of ships. In this paper, we propose a novel coarse-to-fine ship detection method based on discrete wavelet transform (DWT) and a deep residual dense network (DRDN) to address these problems. First, multi-spectral images are adopted for sea-land segmentation, and an enhanced DWT is employed to quickly extract ship candidate regions with missing alarms as low as possible. Second, panchromatic images with clear spatial details are used for ship classification. Specifically, we propose the local residual dense block (LRDB) to fully extract semantic feature via local residual connection and densely connected convolutional layers. DRDN mainly consists of four LRDBs and is designed to further remove false alarms. Furthermore, we exploit the multiclass classification strategy, which can overcome the large intra-class difference of targets and identify ships of different sizes. Extensive experiments demonstrate that the proposed method has high robustness in complex image backgrounds and achieves higher detection accuracy than other state-of-the-art methods.

Maize is moderately sensitive to salt stress; therefore, soil salinity is a serious threat to its production worldwide. Here, excellent salt-tolerant maize inbred line TL1317 and extremely salt-sensitive maize inbred line SL1303 were screened to understand the maize response to salt stress and its tolerance mechanisms. Relative water content, membrane stability index, stomatal conductance, chlorophyll content, maximum photochemical efficiency, photochemical efficiency, shoot and root fresh/dry weight, and proline and water soluble sugar content analyses were used to identify that the physiological effects of osmotic stress of salt stress were obvious and manifested at about 3 days after salt stress in maize. Moreover, the ion concentration of two maize inbred lines revealed that the salt-tolerant maize inbred line could maintain low Na concentration by accumulating Na in old leaves and gradually shedding them to exclude excessive Na . Furthermore, the K uptake and retention abilities of roots were important in maintaining K homeostasis for salt tolerance in maize. RNA-seq and qPCR results revealed some Na /H antiporter genes and Ca transport genes were up-regulated faster and higher in TL1317 than those in SL1303. Some K transport genes were down-regulated in SL1303 but up-regulated in TL1317. RNA-seq results, along with the phenotype and physiological results, suggested that the salt-tolerant maize inbred line TL1317 possesses more rapidly and effectively responses to remove toxic Na ions and maintain K under salt stress than the salt-sensitive maize inbred line SL1303. This response should facilitate cell homoeostasis under salt stress and result in salt tolerance in TL1317.

Background Rubisco activase (RCA) regulates the activity of Rubisco and is a key enzyme of photosynthesis. RCA expression was widely reported to affect plant photosynthesis and crop yield, but the molecular basis of natural variation in RCA expression in a wide range of maize materials has not been fully elucidated. Results In this study, correlation analysis in approximately 200 maize inbred lines revealed a significantly positive correlation between the expression of maize RCA gene ZmRCAβ and grain yield. A genome-wide association study revealed both cis -expression quantitative trait loci ( cis -eQTLs) and trans -eQTLs underlying the expression of ZmRCAβ , with the latter playing a more important role. Further allele mining and genetic transformation analysis showed that a 2-bp insertion and a 14-bp insertion in the promoter of ZmRCAβ conferred increased gene expression. Because rice is reported to have higher RCA gene expression than does maize, we subsequently compared the genetic factors underlying RCA gene expression between maize and rice. The promoter activity of the rice RCA gene was shown to be stronger than that of the maize RCA gene, suggesting that replacing the maize RCA gene promoter with that of the rice RCA gene would improve the expression of RCA in maize. Conclusion Our results revealed two DNA polymorphisms regulating maize RCA gene ZmRCAβ expression, and the RCA gene promoter activity of rice was stronger than that of maize. This work increased understanding of the genetic mechanism that underlies RCA gene expression and identify new targets for both genetic engineering and selection for maize yield improvement.

Defect detection has been a focal point in fabric inspection research and remains challenging. In this paper, a novel method for fabric defect detection is presented. In the proposed algorithm, only defect-free fabric images are used to build the over-complete basis set via sparse coding. Compared to traditional defect detection methods via sparse coding, our method uses a Gabor filter to reduce the complexity of the fabric signal, and takes the fabric patch’s projections in the small scale over-complete basis set as the original features, not the sparse representation. We compare the averages of the patch and its neighborhoods’ features with the standard features, which are the averages of all defect-free fabric images’ features. At last, according to this compared distance, the patch is classified as defective or non-defective. The experimental results on our own database and the TILDA database reveal that our features are more robust and the proposed algorithm can detect defects on twill, plain, gingham and striped fabric effectively.

The purpose of saliency detection is to detect significant regions in the image. Great progress on salient object detection has been made using from deep-learning frameworks. How to effectively extract and integrate multiscale information with different depths is an open problem for salient object detection. In this paper, we propose a processing mechanism based on a balanced attention module and interactive residual module. The mechanism addressed the acquisition of the multiscale features by capturing shallow and deep context information. For effective information fusion, a modified bi-directional propagation strategy was adopted. Finally, we used the fused multiscale information to predict saliency features, which were combined to generate the final saliency maps. The experimental results on five benchmark datasets show that the method is on a par with the state of the art for image saliency datasets, especially on the PASCAL-S datasets, where the MAE reaches 0.092, and on the DUT-OMROM datasets, where the F-measure reaches 0.763.