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Urban agglomerations have become a new geographical unit in China, breaking the administrative fortresses between cities, which means that the population and economic activities between cities will become more intensive in the future. Constructing and optimizing the ecological security pattern of urban agglomerations is important for promoting harmonious social-economic development and ecological protection. Using the Harbin-Changchun urban agglomeration as a case study, we have identified ecological sources based on the evaluation of ecosystem functions. Based on the resistance surface modified by nighttime light (NTL) data, the potential ecological corridors were identified using the least-cost path method, and key ecological corridors were extracted using the gravity model. By combining 15 ecological sources, 119 corridors, 3 buffer zones, and 77 ecological nodes, the ecological security pattern (ESP) was constructed. The main land-use types composed of ecological sources and corridors are forest land, cultivated land, grassland, and water areas. Some ecological sources are occupied by construction, while unused land has the potential for ecological development. The ecological corridors in the central region are distributed circularly and extend to southeast side in the form of tree branches with the Songhua River as the central axis. Finally, this study proposes an optimizing pattern with "four belts, four zones, one axis, nine corridors, ten clusters and multi-centers" to provide decision makers with spatial strategies with respect to the conflicts between urban development and ecological protection during rapid urbanization.

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Background Bacterial colony morphology is the first step of classifying the bacterial species before sending them to subsequent identification process with devices, such as VITEK 2 automated system and mass spectrometry microbial identification system. It is essential as a pre-screening process because it can greatly reduce the scope of possible bacterial species and will make the subsequent identification more specific and increase work efficiency in clinical bacteriology. But this work needs adequate clinical laboratory expertise of bacterial colony morphology, which is especially difficult for beginners to handle properly. This study presents automatic programs for bacterial colony classification task, by applying the deep convolutional neural networks (CNN), which has a widespread use of digital imaging data analysis in hospitals. The most common 18 bacterial colony classes from Peking University First Hospital were used to train this framework, and other images out of these training dataset were utilized to test the performance of this classifier. Results The feasibility of this framework was verified by the comparison between predicted result and standard bacterial category. The classification accuracy of all 18 bacteria can reach 73%, and the accuracy and specificity of each kind of bacteria can reach as high as 90%. Conclusions The supervised neural networks we use can have more promising classification characteristics for bacterial colony pre-screening process, and the unsupervised network should have more advantages in revealing novel characteristics from pictures, which can provide some practical indications to our clinical staffs.

Diaryl sulfates were successfully applied as one-by-one organo electrophiles in Kumada coupling to construct biaryls with the emission of harmless inorganic salts.