In wireless sensor networks (WSNs)

The type III secretion system (T3SS) is an important genetic determinant that mediates interactions between Gram-negative bacteria and their eukaryotic hosts. Our understanding of the T3SS continues to expand, yet the availability of new bacterial genomes prompts questions about its diversity, distribution and evolution. Through a comprehensive survey of ∼20 000 bacterial genomes, we identified 174 non-redundant T3SSs from 109 genera and 5 phyla. Many of the bacteria are environmental strains that have not been reported to interact with eukaryotic hosts, while several species groups carry multiple T3SSs. Four ultra-conserved Microsynteny Blocks (MSBs) were defined within the T3SSs, facilitating comprehensive clustering of the T3SSs into 13 major categories, and establishing the largest diversity of T3SSs to date. We subsequently extended our search to identify type III effectors, resulting in 8740 candidate effectors. Lastly, an analysis of the key transcriptional regulators and circuits for the T3SS families revealed that low-level T3SS regulators were more conserved than higher-level regulators. This comprehensive analysis of the T3SSs and their protein effectors provides new insight into the diversity of systems used to facilitate host-bacterial interactions.

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In the present study, we constructed an "applied core collection" for phosphorus (P) efficiency of soybean germplasm using a GIS-assisted approach. Systematic characterization and comparative analysis of root architecture were conducted to evaluate the relationship between root architecture and P efficiency and its possible evolutionary pattern. Our results found that: i) root architecture was closely related to P efficiency in soybean. Shallow root architecture had better spatial configuration in the P-rich cultivated soil layer hence higher P efficiency and soybean yield; ii) there was a possible co-evolutionary pattern among shoot type, root architecture and P efficiency. The bush cultivated soybean had a shallow root architecture and high P efficiency, the climbing wild soybean had a deep root architecture and low P efficiency, while the root architecture and P efficiency of semi-wild soybean were intermediate between cultivated and wild soybean; iii) P availability regulated root architecture. Soybean roots became shallower with P addition to the topsoil, indicating that the co-evolutionary relationship between root architecture and P efficiency might be attributed to the long-term effects of topsoil fertilization. Our results could provide important theoretical basis for improving soybean root traits and P efficiency.

Unplanned urban settlements exist worldwide. The geospatial information of these areas is critical for urban management and reconstruction planning but usually unavailable. Automatically characterizing individual buildings in the unplanned urban village using remote sensing imagery is very challenging due to complex landscapes and high-density settlements. The newly emerging deep learning method provides the potential to characterize individual buildings in a complex urban village. This study proposed an urban village mapping paradigm based on U-net deep learning architecture. The study area is located in Guangzhou City, China. The Worldview satellite image with eight pan-sharpened bands at a 0.5-m spatial resolution and building boundary vector file were used as research purposes. There are ten sites of the urban villages included in this scene of the Worldview image. The deep neural network model was trained and tested based on the selected six and four sites of the urban village, respectively. Models for building segmentation and classification were both trained and tested. The results indicated that the U-net model reached overall accuracy over 86% for building segmentation and over 83% for the classification. The F1-score ranged from 0.9 to 0.98 for the segmentation, and from 0.63 to 0.88 for the classification. The Interaction over Union reached over 90% for the segmentation and 86% for the classification. The superiority of the deep learning method has been demonstrated through comparison with Random Forest and object-based image analysis. This study fully showed the feasibility, efficiency, and potential of the deep learning in delineating individual buildings in the high-density urban village. More importantly, this study implied that through deep learning methods, mapping unplanned urban settlements could further characterize individual buildings with considerable accuracy.

What is known and objective: Capsaicin, the major active ingredient of chili pepper, may play a "dual role" in tumourigenesis, acting as a carcinogen or as a cancer preventive agent. The aim of this study was to investigate the anticancer mechanisms of capsaicin and the effects of capsaicin on traditional chemotherapeutic drugs and radiotherapy in various cancer types and the potential for clinical application in cancer therapy. Methods: We conducted extensive literature searches through PubMed to collect representative studies of capsaicin in different cancer types. These studies investigated the anticancer molecular mechanisms of capsaicin.

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