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Houttuynia cordata (H. cordata) is the most common herb as a food and traditional Chinese medicine. Currently, studies on its toxicity have mainly focused on hepatotoxicity. However, its potential embryotoxicity by long-term exposure is often overlooked. Objective: To investigate the effects of H. cordata on embryonic development and its toxicity mechanism by combining network pharmacology, molecular docking, and in vitro experimental methods. Methods: The effects of H. cordata on embryos were evaluated. Zebrafish embryos and embryoid bodies were administered to observe the effects of H. cordata on embryonic development. Based on network pharmacological analysis, it was found that the main active agents producing toxicity in H. cordata were oleanolic acid, lignan, and aristolactam AII. H. cordata can affect PI3K-Akt, MAPK, and Ras signaling pathways by regulating targets, such as AKT1, EGFR, CASP3, and IGF-1. RT-PCR and immunohistochemistry results showed that the expression of AKT1 and PI3K in the embryoid body was significantly reduced after drug administration (p < 0.05). Conclusions: The results of network pharmacology and in vitro experiments suggest that H. cordata may affect embryonic development by influencing the PI3K-Akt signaling pathway.

Salt stress is one of the major environmental threats to plant growth and development. However, the mechanisms of plants responding to salt stress are not fully understood. Through genetic screening, we identified and characterized a salt-sensitive mutant, ses5 (sensitive to salt 5), in Arabidopsis thaliana. Positional cloning revealed that the decreased salt-tolerance of ses5 was caused by a mutation in the transcription factor BP (BREVIPEDICELLUS). BP regulates various developmental processes in plants. However, the biological function of BP in abiotic stress-signaling and tolerance are still not clear. Compared with wild-type plants, the bp mutant exhibited a much shorter primary-root and lower survival rate under salt treatment, while the BP overexpressors were more tolerant. Further analysis showed that BP could directly bind to the promoter of XTH7 (xyloglucan endotransglucosylase/hydrolase 7) and activate its expression. Resembling the bp mutant, the disruption of XTH7 gave rise to salt sensitivity. These results uncovered novel roles of BP in positively modulating salt-stress tolerance, and illustrated a putative working mechanism.

B4C-particle-reinforced Al (B4Cp/Al) composites are widely used in various areas, e.g., armors, electronic packaging and fuel storage, owing to their several outstanding properties including high specific rigidity, excellent wear resistance and light weight. Selective laser melting (SLM) is favored in manufacturing complex components because of its high raw material utilization rate and high efficiency. In this work, a B4Cp/Al composite was successfully synthesized by SLM, and the effects of one of the most important parameters, scanning speed (100–700 mm/s), on the phase composition, density, microhardness and tribological properties of the samples were investigated. The microhardness, relative density and dry-sliding wear resistance of as-prepared B4Cp/Al composites were improved with the decrease in scanning speed, and the sample fabricated at a scanning speed of 100 mm/s exhibited a relative density as high as about 97.1%, and a maximum microhardness of ~180 HV0.1 (approximately six times more than that of the SLM-formed pure Al sample, 31 HV0.1), a minimum wear rate of 4.2 × 10−5 mm3·N−1·m−1 and a corresponding friction coefficient of 0.41. In addition, abrasive wear, adhesive wear and oxidation wear were found to be behind the overall wear behavior of as-prepared B4Cp/Al composites.

Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed.

Current models cannot distinguish detailed topological relations between regions with holes. In order to solve this problem, a new detailed representation model for topological relations is proposed in this study. In this model, the key is to describe topological relations caused by multi-holes. Definitions of regions with holes and real objects expressed by elements of regions with holes are comprehensively analyzed, and then a practical definition of regions with holes is presented. Based on the 9-intersection model, a generalized 9-intersection model is proposed, which can completely describe detailed topological relations between regions with holes. For showing the description ability, the model is applied to describe detailed topological relations between regions with holes and detailed topological relations between objects of different complexities with the same major categories of topological relations, and a case study is designed to show the practicality of the model. The results show that the model is valid.

Osmanthus serrulatus Rehder (Oleaceae) is an endemic spring-flowering species in China. It is narrowly distributed in the southwestern Sichuan Basin, and is facing the unprecedented threat of extinction due to problems associated with natural regeneration, habitat fragmentation and persistent and serious human interference. Here, the genetic diversity and population structure of 262 individuals from ten natural populations were analyzed using 18 microsatellites (SSR) markers. In total, 465 alleles were detected across 262 individuals, with a high polymorphic information content (PIC = 0.893). A high level of genetic diversity was inferred from the genetic diversity parameters (He = 0.694, I = 1.492 and PPL = 98.33%). AMOVA showed that a 21.55% genetic variation existed among populations and the mean pairwise Fst (0.215) indicated moderate genetic population differentiation. The ten populations were basically divided into three groups, including two obviously independent groups. Our results indicate that multiple factors were responsible for the complicated genetic relationship and endangered status of O. serrulatus. The concentrated distribution seems to be the key factor causing endangerment, and poor regeneration, human-induced habitat loss and fragmentation seem to be the primary factors in the population decline and further genetic diversity loss. These findings will assist in future conservation management and the scientific breeding of O. serrulatus.

Building Change Detection (BCD) is one of the core issues in earth observation and has received extensive attention in recent years. With the rapid development of earth observation technology, the data source of remote sensing change detection is continuously enriched, which provides the possibility to describe the spatial details of the ground objects more finely and to characterize the ground objects with multiple perspectives and levels. However, due to the different physical mechanisms of multi-source remote sensing data, BCD based on heterogeneous data is a challenge. Previous studies mostly focused on the BCD of homogeneous remote sensing data, while the use of multi-source remote sensing data and considering multiple features to conduct 2D and 3D BCD research is sporadic. In this article, we propose a novel and general squeeze-and-excitation W-Net, which is developed from U-Net and SE-Net. Its unique advantage is that it can not only be used for BCD of homogeneous and heterogeneous remote sensing data respectively but also can input both homogeneous and heterogeneous remote sensing data for 2D or 3D BCD by relying on its bidirectional symmetric end-to-end network architecture. Moreover, from a unique perspective, we use image features that are stable in performance and less affected by radiation differences and temporal changes. We innovatively introduced the squeeze-and-excitation module to explicitly model the interdependence between feature channels so that the response between the feature channels is adaptively recalibrated to improve the information mining ability and detection accuracy of the model. As far as we know, this is the first proposed network architecture that can simultaneously use multi-source and multi-feature remote sensing data for 2D and 3D BCD. The experimental results in two 2D data sets and two challenging 3D data sets demonstrate that the promising performances of the squeeze-and-excitation W-Net outperform several traditional and state-of-the-art approaches. Moreover, both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed network. This demonstrates that the proposed network and method are practical, physically justified, and have great potential application value in large-scale 2D and 3D BCD and qualitative and quantitative research.

In recent decades, high-resolution (HR) remote sensing images have shown considerable potential for providing detailed information for change detection. The traditional change detection methods based on HR remote sensing images mostly only detect a single land type or only the change range, and cannot simultaneously detect the change of all object types and pixel-level range changes in the area. To overcome this difficulty, we propose a new coarse-to-fine deep learning-based land-use change detection method. We independently created a new scene classification dataset called NS-55, and innovatively considered the adaptation relationship between the convolutional neural network (CNN) and the scene complexity by selecting the CNN that best fit the scene complexity. The CNN trained by NS-55 was used to detect the category of the scene, define the final category of the scene according to the majority voting method, and obtain the changed scene by comparison to obtain the so-called coarse change result. Then, we created a multi-scale threshold (MST) method, which is a new method for obtaining high-quality training samples. We used the high-quality samples selected by MST to train the deep belief network to obtain the pixel-level range change detection results. By mapping coarse scene changes to range changes, we could obtain fine multi-type land-use change detection results. Experiments were conducted on the Multi-temporal Scene Wuhan dataset and aerial images of a particular area of Dapeng New District, Shenzhen, where promising results were achieved by the proposed method. This demonstrates that the proposed method is practical, easy-to-implement, and the NS-55 dataset is physically justified. The proposed method has the potential to be applied in the large scale land use fine change detection problem and qualitative and quantitative research on land use/cover change based on HR remote sensing data.

Background: The C-terminally encoded peptide (CEP) family has been shown to play vital roles in plant growth. Although a genome-wide analysis of this family has been performed in Arabidopsis, little is known regarding CEPs in apple (Malus domestica). Methods: Here, a comprehensive bioinformatics approach was applied to identify MdCEPs in apple, and 12 MdCEP genes were identified and distributed on 6 chromosomes. Results: MdCEP1 peptide had an inhibitory effect on root growth of apple seedlings, indicating that MdCEP1 played a negative role in root development. In addition, the serine and glycine residues remained conserved within the CEP domains, and MdCEP1 lost its function after mutation of these two key amino acids, suggesting that Ser10 and Gly14 residues are crucial for MdCEPs-mediated root growth of apple. Encouragingly, multiple sequence alignment of 273 CEP domains showed that Ser10 residue was evolutionarily conserved in monocot and eudicot plants. MdCEP derivative (Ser to Cys) lost the ability to inhibit the root growth of Nicotiana benthamiana, Setaria italic, Samolous parviflorus, and Raphanus sativus L. and up-regulate the NO3− importer gene NRT2.1. Conclusion: Taken together, Ser10 residue is crucial for CEP function exertion in higher land plants, at least in apple.

Swainsonine (SW) is a toxin produced by locoweeds and harmful to the livestock industry. Degrading SW by Arthrobacter sp. HW08 was demonstrated as a promising way to deal with SW poisoning. However, it is unknown which part of the subcellular enzymes in Arthrobacter sp. HW08 is responsible for biodegrading SW and whether the metabolites are atoxic. In this study, intracellular and extracellular enzymes of Arthrobacter sp. HW08 were isolated and their enzyme activity was evaluated. The metabolites were fed to mice, and physiological and histological properties of the treated mice were investigated. The results showed that only intracellular enzyme of Arthrobacter sp. HW08 (IEHW08) could degrade SW efficiently. Compared with mice in SW treatment group, mice in SW + IEHW08 treatment group (1) increased their body weights; (2) showed higher number of platelets and lower number of white blood cells; (3) decreased the levels of creatinine, urea nitrogen, alanine transaminase and aspartate aminotransferase in serum; (4) reduced the number of vacuolated cells in cerebellum, liver and kidney. All these data demonstrate that IEHW08 was potentially safe for mice, while keeping the capacity of degrading SW. This study indicates a possible application of IEHW08 as an additive in the livestock industry to protect animals from SW poisoning.

Remote sensing and GIS have been widely employed to study temporal and spatial urban land use changes in southern and southeastern China. However, few studies have been conducted in northeastern regions. This study analyzed land use change and spatial patterns of urban expansion in the metropolitan area of Jilin City, located on the extension of Changbai Mountain, based on aerial photos from 1989 and 2005 Spot images. The results indicated that urban land and transportation land increased dramatically (by 94.04% and 211.20%, respectively); isolated industrial and mining land decreased moderately (by 29.54%); rural residential land increased moderately (by 26.48%); dry land and paddy fields increased slightly (by 15.68% and 11.78%, respectively); forest and orchards decreased slightly (by 5.27% and 4.61%, respectively); grasslands and unused land decreased dramatically (by 99.12% and 86.04%, respectively). Sloped dry land (more than 4 degrees) was mainly distributed on the land below 10 degrees with an east, southeastern and south sunny direction aspect, and most sloped dry land transformed to forest was located on an east aspect lower than 12 degrees, while forest changed to dry land were mainly distributed on east and south aspects lower than 10 degrees. A spatial dependency analysis of land use change showed that the increased urban land was a logarithmic function of distance to the Songhua River. This study also provided some data with spatial details about the uneven land development in the upstream areas of Songhua River basin.

Changes in river plane shapes are called river planform changes (RPCs). Such changes can impact sustainable human development (e.g., human habitations, industrial and agricultural development, and national border security). RPCs can be identified through field surveys—a method that is highly precise but time-consuming, or through remote sensing (RS) and geographic information system (GIS), which are less precise but more efficient. Previous studies that have addressed RPCs often used RS, GIS, or digital elevation models (DEMs) and focused on only one or a few rivers in specific areas with the goal of identifying the reasons underlying these changes. In contrast, in this paper, we developed a combinatorial reasoning mechanism based on topological and metric relations that can be used to classify RPCs. This approach does not require DEMs and can eliminate most false-change information caused by varying river water levels. First, we present GIS models of river planforms based on their natural properties and, then, modify these models into simple GIS river planform models (SGRPMs) using straight lines rather than common lines to facilitate computational and human understanding. Second, we used double straight line 4-intersection models (DSL4IMs) and intersection and difference models (IDMs) of the regions to represent the topological relations between the SGRPMs and used double-start-point 8-distance models (DS8DMs) to express the metric relations between the SGRPMs. Then, we combined topological and metric relations to analyse the changes in the SGRPMs. Finally, to compensate for the complexity of common river planforms in nature, we proposed three segmentation rules to turn common river planforms into SGRPMs and used combinatorial reasoning mechanism tables (CRMTs) to describe the spatial relations among different river planforms. Based on our method, users can describe common river planforms and their changes in detail and confidently reject false changes. Future work should develop a method to automatically or semi-automatically adjust the segmentation rules and the combinatorial reasoning mechanism.