Osteoporosis is a common metabolic bone disease, influenced by genetic and environmental factors, that increases bone fragility and fracture risk and, therefore, has a serious adverse effect on the quality of life of patients. However, epigenetic mechanisms involved in the development of osteoporosis remain unclear. There is accumulating evidence that epigenetic modifications may represent mechanisms underlying the links of genetic and environmental factors with increased risk of osteoporosis and bone fracture. Some RNAs, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), have been shown to be epigenetic regulators with significant involvement in the control of gene expression, affecting multiple biological processes, including bone metabolism. This review summarizes the results of recent studies on the mechanisms of miRNA-, lncRNA-, and circRNA-mediated osteoporosis associated with osteoblasts and osteoclasts. Deeper insights into the roles of these three classes of RNA in osteoporosis could provide unique opportunities for developing novel diagnostic and therapeutic approaches to this disease.

Legal Judgment Prediction (LJP) aims to predict the judgment result based on the facts of a case and becomes a promising application of artificial intelligence techniques in the legal field. In real-world scenarios, legal judgment usually consists of multiple subtasks, such as the decisions of applicable law articles, charges, fines, and the term of penalty. Moreover, there exist topological dependencies among these subtasks. While most existing works only focus on a specific subtask of judgment prediction and ignore the dependencies among subtasks, we formalize the dependencies among subtasks as a Directed Acyclic Graph (DAG) and propose a topological multi-task learning framework, TOP-JUDGE, which incorporates multiple subtasks and DAG dependencies into judgment prediction. We conduct experiments on several realworld large-scale datasets of criminal cases in the civil law system. Experimental results show that our model achieves consistent and significant improvements over baselines on all judgment prediction tasks. The source code can be obtained from https://github. com/thunlp/TopJudge.

Automatic building segmentation from aerial imagery is an important and challenging task because of the variety of backgrounds, building textures and imaging conditions. Currently, research using variant types of fully convolutional networks (FCNs) has largely improved the performance of this task. However, pursuing more accurate segmentation results is still critical for further applications such as automatic mapping. In this study, a multi-constraint fully convolutional network (MC-FCN) model is proposed to perform end-to-end building segmentation. Our MC-FCN model consists of a bottom-up/top-down fully convolutional architecture and multi-constraints that are computed between the binary cross entropy of prediction and the corresponding ground truth. Since more constraints are applied to optimize the parameters of the intermediate layers, the multi-scale feature representation of the model is further enhanced, and hence higher performance can be achieved. The experiments on a very-high-resolution aerial image dataset covering 18 km 2 and more than 17,000 buildings indicate that our method performs well in the building segmentation task. The proposed MC-FCN method significantly outperforms the classic FCN method and the adaptive boosting method using features extracted by the histogram of oriented gradients. Compared with the state-of-the-art U-Net model, MC-FCN gains 3.2% (0.833 vs. 0.807) and 2.2% (0.893 vs. 0.874) relative improvements of Jaccard index and kappa coefficient with the cost of only 1.8% increment of the model-training time. In addition, the sensitivity analysis demonstrates that constraints at different positions have inconsistent impact on the performance of the MC-FCN.

No abstract