Graphene grids exhibit exceptional loading capacity for macromolecules,single atoms,and nanoparticles,offering significant potential for exploring the structure and properties of various materials at the nanoscale.How...Graphene grids exhibit exceptional loading capacity for macromolecules,single atoms,and nanoparticles,offering significant potential for exploring the structure and properties of various materials at the nanoscale.However,challenges such as carbon film rupture,contamination,and uneven graphene film coverage frequently occur during grid fabrication.Here wepropose a dual-stage deep learning model integrating U-Net and an enhanced YOLO11 architecture,enabling efficient and accurate defect detection and graphene coverage quantification.A tailored data augmentation strategy expanded the initial defect dataset by more than an order of magnitude,which directly contributed to an overall 11.72%improvement across the model’s performance metrics.With the integration of the multi-scale convolutional attention(MSCA)module and the slicing-aided hyper inference(SAHI)method,the model achieved a 0.67%mean absolute percentage error(MAPE),while reducing the average detection time from 26.6 to 0.1 min per image.The proposed model holds strong potential for extension to various material characterization image analysis tasks,providing a scalable strategy for high-throughput image processing that bridges fundamental research with industrialscale applications.展开更多
Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidence shows that these microbes are important for plant growth and health. Root microbiota compo...Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidence shows that these microbes are important for plant growth and health. Root microbiota composition has been widely studied in several model plants and crops; however, little is known about how root microbiota vary throughout the plant's life cycle under field conditions. We performed longitudinal dense sampling in field trials to track the time-series shift of the root microbiota from two representative rice cultivars in two separate locations in China. We found that the rice root microbiota varied dramatically during the vegetative stages and stabilized from the beginning of the reproductive stage, after which the root microbiota underwent relatively minor changes until rice ripening. Notably, both rice genotype and geographical location influenced the patterns of root microbiota shift that occurred during plant growth. The relative abundance of Deltaproteobacteria in roots significantly increased overtime throughout the entire life cycle of rice, while that of Betaproteobacteria, Firmicutes, and Gammaproteobacteria decreased. By a machine learning approach, we identified biomarker taxa and established a model to correlate root microbiota with rice resident time in the field(e.g., Nitrospira accumulated from 5 weeks/tillering in field-grown rice). Our work provides insights into the process of rice root microbiota establishment.展开更多
基金supported by National Key Research and Development Program of China(2024YFB4709300)the National Natural Science Foundation of China(No.52130501,52505289)+1 种基金Zhejiang provincial teams of leading talents in Innovation and Entrepreneurship(2024R01002)Guizhou Provincial Science and Technology Projects(XKBF[2025]014,BQW[2024]010).
文摘Graphene grids exhibit exceptional loading capacity for macromolecules,single atoms,and nanoparticles,offering significant potential for exploring the structure and properties of various materials at the nanoscale.However,challenges such as carbon film rupture,contamination,and uneven graphene film coverage frequently occur during grid fabrication.Here wepropose a dual-stage deep learning model integrating U-Net and an enhanced YOLO11 architecture,enabling efficient and accurate defect detection and graphene coverage quantification.A tailored data augmentation strategy expanded the initial defect dataset by more than an order of magnitude,which directly contributed to an overall 11.72%improvement across the model’s performance metrics.With the integration of the multi-scale convolutional attention(MSCA)module and the slicing-aided hyper inference(SAHI)method,the model achieved a 0.67%mean absolute percentage error(MAPE),while reducing the average detection time from 26.6 to 0.1 min per image.The proposed model holds strong potential for extension to various material characterization image analysis tasks,providing a scalable strategy for high-throughput image processing that bridges fundamental research with industrialscale applications.
基金supported by the“Strategic Priority Research Program”of the Chinese Academy of Sciences(XDB11020700)CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellows(2016LH00012)+1 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(QYZDB-SSW-SMC021)the National Natural Science Foundation of China(31772400)
文摘Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidence shows that these microbes are important for plant growth and health. Root microbiota composition has been widely studied in several model plants and crops; however, little is known about how root microbiota vary throughout the plant's life cycle under field conditions. We performed longitudinal dense sampling in field trials to track the time-series shift of the root microbiota from two representative rice cultivars in two separate locations in China. We found that the rice root microbiota varied dramatically during the vegetative stages and stabilized from the beginning of the reproductive stage, after which the root microbiota underwent relatively minor changes until rice ripening. Notably, both rice genotype and geographical location influenced the patterns of root microbiota shift that occurred during plant growth. The relative abundance of Deltaproteobacteria in roots significantly increased overtime throughout the entire life cycle of rice, while that of Betaproteobacteria, Firmicutes, and Gammaproteobacteria decreased. By a machine learning approach, we identified biomarker taxa and established a model to correlate root microbiota with rice resident time in the field(e.g., Nitrospira accumulated from 5 weeks/tillering in field-grown rice). Our work provides insights into the process of rice root microbiota establishment.
