Real-time building damage detection effectively improves the timeliness of post-earthquake assessments. In recent years, terrestrial images from smartphones or cameras have become a rich source of disaster information that may be useful in assessing building damage at a lower cost. In this study, we present an efficient method of building damage detection based on terrestrial images in combination with an improved YOLOv5. We compiled a Ground-level Detection in Building Damage Assessment (GDBDA) dataset consisting of terrestrial images with annotations of damage types, including debris, collapse, spalling, and cracks. A lightweight and accurate YOLOv5 (LA-YOLOv5) model was used to optimize the detection efficiency and accuracy. In particular, a lightweight Ghost bottleneck was added to the backbone and neck modules of the YOLOv5 model, with the aim to reduce the model size. A Convolutional Block Attention Module (CBAM) was added to the backbone module to enhance the damage recognition effect. In addition, regarding the scale difference of building damage, the Bi-Directional Feature Pyramid Network (Bi-FPN) for multi-scale feature fusion was used in the neck module to aggregate features with different damage types. Moreover, depthwise separable convolution (DSCONV) was used in the neck module to further compress the parameters. Based on our GDBDA dataset, the proposed method not only achieved detection accuracy above 90% for different damage targets, but also had the smallest weight size and fastest detection speed, which improved by about 64% and 24%, respectively. The model performed well on datasets from different regions. The overall results indicate that the proposed model realizes rapid and accurate damage detection, and meets the requirement of lightweight embedding in the future.

Accurate assessment of building damage is very important for disaster response and rescue. Traditional damage detection techniques using 2D features at a single observing angle cannot objectively and accurately reflect the structural damage conditions. With the development of unmanned aerial vehicle photogrammetric techniques and 3D point processing, automatic and accurate damage detection for building roof and facade has become a research hotspot in recent work. In this paper, we propose a building damage detection framework based on the boundary refined supervoxel segmentation and random forest–latent Dirichlet allocation classification. First, the traditional supervoxel segmentation method is improved to segment the point clouds into good boundary refined supervoxels. Then, non-building points such as ground and vegetation are removed from the generated supervoxels. Next, latent Dirichlet allocation (LDA) model is used to construct the high-level feature representation for each building supervoxel based on the selected 2D image and 3D point features. Finally, LDA model and random forest algorithm are employed to identify the damaged building regions. This method is applied to oblique photogrammetric point clouds collected from the Beichuan Country Earthquake Site. The research achieves the 3D damage assessment for building facade and roof. The result demonstrates that the proposed framework is capable of achieving around 94% accuracy for building point extraction and around 90% accuracy for damage identification. Moreover, both of the precision and recall for building damage detection reached around 89%. Concluded from comparison analysis, the proposed method improved the damage detection accuracy and the highest improvement ratio is over 8%.

Leaf angle and leaf orientation value (LOV) are critical agronomic traits for maize plant architecture. The functions of NUCLEAR FACTOR Y (NF-Y) members in regulating plant architecture have not been reported yet. Here, we identified a regulator of maize plant architecture, NF-Y subunit C13 (ZmNF-YC13). ZmNF-YC13 was highly expressed in the leaf base zone of maize plants. ZmNF-YC13 overexpressing plants showed upright leaves with narrow leaf angle and larger LOV, while ZmNF-YC13 knockout plants had larger leaf angle and smaller LOV compared with wild-type plants. The changes in plant architecture were due to the changes in the expression of cytochrome P450 family members. ZmNF-YC13 interacts with two NF-Y subunit B members (ZmNF-YB9 and ZmNF-YB10) of the LEAFY COTYLEDON1 sub-family, and further recruits NF-Y subunit A (ZmNF-YA3) to form two NF-Y complexes. The two complexes can both activate the promoters of transcriptional repressors (ZmWRKY76 and ZmBT2), and the promoters of PLASTOCHRON group genes can be repressed by ZmWRKY76 and ZmBT2 in maize protoplasts. We propose that ZmNF-YC13 functions as a transcriptional regulator and, together with ZmNF-YBs and ZmNF-YA3, affects plant architecture by regulating the expression of ZmWRKY76 and ZmBT2, which repress the expression of cytochrome P450 family members in PLASTOCHRON branch.

Carbonic anhydrase (CA) catalyzes the reversible CO2 hydration reaction that produces bicarbonate for phosphoenolpyruvate carboxylase (PEPC). This is the initial step for transmitting the CO2 signal in C4 photosynthesis. However, it remains unknown whether the maize (Zea mays L.) CA gene, ZmCA4, plays a role in the maize photosynthesis process. In our study, we found that ZmCA4 was relatively highly expressed in leaves and localized in the chloroplast and the plasma membrane of mesophyll protoplasts. Knock-out of ZmCA4 reduced CA activity, while overexpression of ZmCA4 increased rubisco activity, as well as the quantum yield and relative electron transport rate in photosystem II. Overexpression of ZmCA4 enhanced maize yield–related traits. Moreover, ZmCA4 interacted with aquaporin ZmPIP2;6 in bimolecular fluorescence complementation and co-immunoprecipitation experiments. The double-knock-out mutant for ZmPIP2;6 and ZmCA4 genes showed reductions in its growth, CA and PEPC activities, assimilation rate and photosystem activity. RNA-Seq analysis revealed that the expression of other ZmCAs, ZmPIPs, as well as CO2 signaling pathway homologous genes, and photosynthetic-related genes was all altered in the double-knock-out mutant compared with the wild type. Altogether, our study’s findings point to a critical role of ZmCA4 in determining photosynthetic capacity and modulating CO2 signaling regulation via its interaction with ZmPIP2;6, thus providing insight into the potential genetic value of ZmCA4 for maize yield improvement.

ZAG2 has been identified as a maternally expressed imprinted gene in maize endosperm. Our study revealed that paternally inherited ZAG2 alleles were imprinted in maize endosperm and embryo at 14days after pollination (DAP), and consistently imprinted in endosperm at 10, 12, 16, 18, 20, 22, 24, 26, and 28 DAP in reciprocal crosses between B73 and Mo17. ZAG2 alleles were also imprinted in reciprocal crosses between Zheng 58 and Chang 7-2 and between Huang C and 178. ZAG2 alleles exhibited differential imprinting in hybrids of 178×Huang C and B73×Mo17, while in other hybrids ZAG2 alleles exhibited binary imprinting. The tissue-specific expression pattern of ZAG2 showed that ZAG2 was expressed at a high level in immature ears, suggesting that ZAG2 plays important roles in not only kernel but ear development

An efficient and accurate identification of damaged buildings after earthquakes is critical for ensuring a quick response and efficient rescue operations. Considering the time pressure during disaster emergency response and the convenience of practical application, a new approach for identifying areas with damaged buildings is proposed in this study. The approach is validated on high-resolution images from areas impacted by the Haiti earthquake. The approach consists of the following steps. First, the simple linear iterative clustering method is improved to segment the images into high-quality superpixels by combining both color and texture information. Then, the characteristics of the damage in the area within the cluster and the difference between pre- and post-earthquake images are obtained at the superpixel level using the Sobel gradient. Next, local indicators from the spatial association analysis are used to extract the result of the gradient clustering using the synthetic damage index calculated by an improved relief algorithm. Finally, vegetation and shadows are masked out to identify the damaged buildings. This framework achieved an overall accuracy of ~90% in terms of visual interpretation, with precision and recall of ~85% and ~90%, respectively. Inaccuracies mainly occur around the boundaries of the damaged building regions. It is evaluated that the proposed framework is more convenient compared to other supervised and unsupervised methods. The successful application in different scenes demonstrates that the proposed framework has the ability to rapidly and accurately identify regions with damaged buildings, which is beneficial for post-disaster emergency assessment.