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Background Several studies have investigated the effect of non–vitamin K antagonist oral anticoagulants ( NOAC s) in atrial fibrillation ( AF ) patients with cancer, but the results remain controversial. Therefore, we conducted a meta‐analysis to compare the efficacy and safety of NOAC s versus warfarin in this population. Methods and Results We systematically searched the PubMed and Embase databases until February 16, 2019 for studies comparing the effect of NOAC s with warfarin in AF patients with cancer. Risk ratios ( RR s) with 95% CI s were extracted and pooled by a random‐effects model. Five studies involving 8908 NOAC s and 12 440 warfarin users were included. There were no significant associations between cancer status and risks of stroke or systemic embolism, major bleeding, or death in AF patients. Compared with warfarin, NOAC s were associated with decreased risks of stroke or systemic embolism ( RR , 0.52; 95% CI , 0.28–0.99), venous thromboembolism ( RR , 0.37, 95% CI , 0.22–0.63), and intracranial or gastrointestinal bleeding ( RR , 0.65; 95% CI , 0.42–0.98) and with borderline significant reductions in ischemic stroke ( RR , 0.63; 95% CI , 0.40–1.00) and major bleeding ( RR , 0.73; 95% CI , 0.53–1.00). In addition, risks of efficacy and safety outcomes of NOAC s versus warfarin were similar between AF patients with and without cancer. Conclusions In patients with AF and cancer, compared with warfarin, NOAC s had lower or similar rates of thromboembolic and bleeding events and posed a reduced risk of venous thromboembolism.

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DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.

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Image retrieval is becoming more and more important due to the rapid increase of the number of images on the web. To improve the efficiency of computing the similarity of images, hashing has moved into the focus of research. This paper proposes a Deep Attention-based Hash (DAH) retrieval model, which combines an attention module and a convolutional neural network to obtain hash codes with strong representability. Our DAH has the following features: The Hamming distance between the hash codes generated by similar images is small and the Hamming distance of hash codes of dissimilar images has a larger constant value. The quantitative loss from Euclidean distance to Hamming distance is minimized. DAH has a high image retrieval precision: We thoroughly compare it with ten state-of-the-art approaches on the CIFAR-10 dataset. The results show that the Mean Average Precision (MAP) of DAH reaches more than 92% in terms of 12, 24, 36 and 48 bit hash codes on CIFAR-10, which is better than what the state-ofart methods used for comparison can deliver.