[Identification and restoration strategy of key areas for territorial space ecological restoration in Shanghai, China based on ecological security pattern]. / åŸºäºŽç”Ÿæ€å®‰å ¨æ ¼å±€çš„ä¸Šæµ·å›½åœŸç©ºé—´ç”Ÿæ€ä¿®å¤å ³é”®åŒºåŸŸè¯†åˆ«ä¸Žä¿®å¤ç­–ç•¥.

In the new era, ecological restoration of territorial space is the important task of maintaining regional ecological security, improving regional ecological quality and providing high-quality ecological products. From the perspective of ecological security, accurately determination of key areas to be restored in the territorial space is the primary work, and it is also a key and difficult problem to scientifically carry out ecological restoration. Based on the mainstream ecological security pattern theory, taking Shanghai as the research area, we integrated morphological spatial pattern analysis method and InVEST model to identify ecological sources, extracted ecological corridors, ecological "pinch points" and obstacle points with circuit theory, comprehensively determined the key areas to be restored, and proposed targeted restoration strategies. The results showed that the ecological sources of Shanghai were mainly distributed in the Yangtze River estuary, Chongming Island, Hangzhou Bay coast, and Dianshan Lake, accounting for about 17.9% of the study area. There were 103 key ecological corridors. The key areas to be repaired included 12 ecological "pinch points" and 54 ecological obstacle points, which were mainly distributed at the border of ecological source and ecological corridor, as well asthe intersection or turning point of ecological corridor and ecological corridor. According to the typical problems of key areas to be restored and land use conditions, three types of restoration strategy zones were proposed: ecological landscape reshaping, important corridor penetration, and ecological shoreline protection and restoration. The results could provide reference for compiling a territorial space ecological restoration plan in Shanghai and building a medium-scale ecological security pattern and carrying out systemic ecological restoration work in other regions of China.

Deep Learning for Accurate Diagnosis of Liver Tumor Based on Magnetic Resonance Imaging and Clinical Data.

Background: Early-stage diagnosis and treatment can improve survival rates of liver cancer patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for differential diagnosis of liver tumors. However, MRI diagnosis is affected by subjective experience, so deep learning may supply a new diagnostic strategy. We used convolutional neural networks (CNNs) to develop a deep learning system (DLS) to classify liver tumors based on enhanced MR images, unenhanced MR images, and clinical data including text and laboratory test results. Methods: Using data from 1,210 patients with liver tumors (N = 31,608 images), we trained CNNs to get seven-way classifiers, binary classifiers, and three-way malignancy-classifiers (Model A-Model G). Models were validated in an external independent extended cohort of 201 patients (N = 6,816 images). The area under receiver operating characteristic (ROC) curve (AUC) were compared across different models. We also compared the sensitivity and specificity of models with the performance of three experienced radiologists. Results: Deep learning achieves a performance on par with three experienced radiologists on classifying liver tumors in seven categories. Using only unenhanced images, CNN performs well in distinguishing malignant from benign liver tumors (AUC, 0.946; 95% CI 0.914-0.979 vs. 0.951; 0.919-0.982, P = 0.664). New CNN combining unenhanced images with clinical data greatly improved the performance of classifying malignancies as hepatocellular carcinoma (AUC, 0.985; 95% CI 0.960-1.000), metastatic tumors (0.998; 0.989-1.000), and other primary malignancies (0.963; 0.896-1.000), and the agreement with pathology was 91.9%.These models mined diagnostic information in unenhanced images and clinical data by deep-neural-network, which were different to previous methods that utilized enhanced images. The sensitivity and specificity of almost every category in these models reached the same high level compared to three experienced radiologists. Conclusion: Trained with data in various acquisition conditions, DLS that integrated these models could be used as an accurate and time-saving assisted-diagnostic strategy for liver tumors in clinical settings, even in the absence of contrast agents. DLS therefore has the potential to avoid contrast-related side effects and reduce economic costs associated with current standard MRI inspection practices for liver tumor patients.