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Previous studies found that Central Equatorial Africa (CEA) has experienced a long-term drying trend over the past two decades. To further evaluate this finding, we investigate possible mechanisms for this drought by analyzing multiple sources of observations and reanalysis data. We examine the atmospheric circulation changes related to sea surface temperature (SST) variations that control the equatorial African rainfall. Our results indicate that the long-term drought during April, May and June over CEA may reflect the large-scale response of the atmosphere to tropical SST variations. Likely the drought results primarily from SST variations over Indo-Pacific associated with the enhanced and westward extended tropical Walker circulation. These are consistent with the weakened ascent over Central Africa that is associated with the reduced low-level moisture transport. The large-scale atmospheric circulation changes associated with a weaker West African monsoon also have some contribution. These results reinforce the notion that tropical SSTs have large impacts on rainfall over equatorial Africa and highlight the need to further distinguish the contribution of SSTs changes (e.g., La Niña-like pattern and Indian Ocean warming) due to natural variability and anthropogenic forcing to the drought.

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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 malignancyclassifiers (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 deepneural-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. Zhen et al. Deep Learning for Liver Tumor Diagnosis 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.

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