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Background: Acne is one of the most common skin lesions in adolescents. Some severe or inflammatory acne leads to scars, which may have major impacts on patients' quality of life or even job prospects. Grading acne plays an important role in diagnosis, and the diagnosis is made by counting the number of acne. It is a labor‐intensive job and it is easy for dermatologists to make mistakes, so it is very important to develop automatic diagnosis methods. Ensemble learning may improve the prediction results of the base models, but its time complexity is relatively high. The ensemble pruning strategy may solve this computational challenge by removing the redundant base models. Materials and methods: This study proposed a novel ensemble pruning framework of deep learning models to accurately detect and grade acne using images. First, we train multi‐base models and prune the redundancy models according to the performance and diversity of the models. Then, we construct the new features of the training data by the base models we select in the previous step. Next, we remove the redundancy models further by a feature selection algorithm. Finally, we integrate all the base models by classifiers. The ensemble pruning algorithm was proposed to prune the deep learning base models. Results: The experimental data showed that the ensemble pruned framework achieved a prediction accuracy of 85.82% on the acne dataset, better than the existing studies. To verify our method's effectiveness, we test our method in a skin cancer dataset and greatly outperform the state‐of‐the‐art methods. Conclusion: The method we proposed is used to grade acne. Our method's performance outperforms state‐of‐the‐art methods on two datasets, and it can also remove redundancy models to reduce computational complexity. [ABSTRACT FROM AUTHOR]

Large-scale forest loss and degradation have dire consequences for biodiversity maintenance and provision of vital ecosystem services. Despite recent increasing efforts for forest restoration and sustainable management, there have been no comparative studies of biological taxonomy and multiple ecosystem functions to assess the effectiveness of forest restoration programmes, and how they vary through space and time. Here, we provided a quantitative assessment of the recovery of biodiversity and ecosystem functions by forest restoration in China using a meta-analysis of 172 studies. We found that biodiversity and ecosystem functions were substantially increased in restored forests comparing with the degraded states. However, these restoration effects varied considerably by degradation origin, restoration approach, restoration age, ecological domains, taxonomic group and ecosystem function that is measured. Results also revealed that forest restoration from degraded states could not lead to full recovery of biodiversity and ecosystem functions, highlighting the irreplaceability of primary forests. We advocate allowing for natural or passive recovery, especially where biophysical conditions are favourable for spontaneous succession, or too harsh for human-aided restoration, and choosing a combination of passive and active restoration measures based on adaptive management strategies. Our meta-analysis provided fundamental insights into bridging the gap between small-scale experiments and broad-scale management needs towards highly effective and sustainable forest ecosystem restoration. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]