As the number of known proteins has expanded, how to accurately identify DNA binding proteins has become a significant biological challenge. At present, various computational methods have been proposed to recognize DNA-binding proteins from only amino acid sequences, such as SVM, DNABP and CNN-RNN. However, these methods do not consider the context in amino acid sequences, which makes it difficult for them to adequately capture sequence features. In this study, a new method that coordinates a bidirectional long-term memory recurrent neural network and a convolutional neural network, called CNN-BiLSTM, is proposed to identify DNA binding proteins. The CNN-BiLSTM model can explore the potential contextual relationships of amino acid sequences and obtain more features than can traditional models. The experimental results show that the CNN-BiLSTM achieves a validation set prediction accuracy of 96.5%—7.8% higher than that of SVM, 9.6% higher than that of DNABP and 3.7% higher than that of CNN-RNN. After testing on 20,000 independent samples provided by UniProt that were not involved in model training, the accuracy of CNN-BiLSTM reached 94.5%—12% higher than that of SVM, 4.9% higher than that of DNABP and 4% higher than that of CNN-RNN. We visualized and compared the model training process of CNN-BiLSTM with that of CNN-RNN and found that the former is capable of better generalization from the training dataset, showing that CNN-BiLSTM has a wider range of adaptations to protein sequences. On the test set, CNN-BiLSTM has better credibility because its predicted scores are closer to the sample labels than are those of CNN-RNN. Therefore, the proposed CNN-BiLSTM is a more powerful method for identifying DNA-binding proteins.

Abstract. Recent advances in networking and sensor technologies allow various physical world objects connected to form the Internet of Things (IOT). As more sensor networks are being deployed in agriculture today, there is a vision of integrating different agriculture IT system into the agriculture IOT. The key challenge of such integration is how to deal with semantic heterogeneity of multiple information resources. The paper proposes an ontology-based approach to describe and extract the semantics of agriculture objects and provides a mechanism for sharing and reusing agriculture knowledge to solve the semantic interoperation problem. AgOnt, ontology for the agriculture IOT, is built from agriculture terminologies and the lifecycles including seeds, grains, transportation, storage and consumption. According to this unified meta-model, heterogeneous agriculture data sources can be integrated and accessed seamlessly.

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This paper explores how the installation of the full-view micro-sensor detector will affect the electric-field distribution of ±800-kV converter valve. We build the 3D model of ±800-kV converter valve tower and use Ansoft Maxwell 3D Field Simulator to study the electric-field distribution of ±800-kV converter valve tower with the full-view micro-sensor detector at different logical suitable locations. The results show that the installed locations have nearly no influence on the maximum electric-field distribution of non-direct contacted components; but for the direct-contacted components, the maximum electric-field has increased to a certain degree. The results further show that the electric-fields distribution especially the maximum electric-fields distribution at different locations is different, but the maximum electric-field value is still less than the 3 kV/mm. Thus, it can be concluded that the installation of full-view micro-sensor detectors can lead to an increase of maximum electric-field, but it has no influence on the insulation condition of valve tower, and the insulation design still meets the requirements well. Finally, the experiment validates that the installation of the full-view micro-sensor detector is feasible.INDEX TERMS Full-view micro-sensor detector, ±800kV converter valve, valve component, electric-field simulation, maximum electric-field, insulation status.