Motivation Transposable elements (TEs) classification is an essential step to decode their roles in genome evolution. With a large number of genomes from non-model species becoming available, accurate and efficient TE classification has emerged as a new challenge in genomic sequence analysis. Results We developed a novel tool, DeepTE, which classifies unknown TEs using convolutional neural networks. DeepTE transferred sequences into input vectors based on k-mer counts. A tree structured classification process was used where eight models were trained to classify TEs into super families and orders. DeepTE also detected domains inside TEs to correct false classification. An additional model was trained to distinguish between non-TEs and TEs in plants. Given unclassified TEs of different species, DeepTE can classify TEs into seven orders, which include 15, 24, and 16 super families in plants, metazoans, and fungi, respectively. In several benchmarking tests, DeepTE outperformed other existing tools for TE classification. In conclusion, DeepTE successfully leverages convolutional neural network for TE classification, and can be used to precisely classify TEs in newly sequenced eukaryotic genomes. Availability DeepTE is accessible at https://github.com/LiLabAtVT/DeepTE Supplementary information Supplementary data are available at Bioinformatics online.

Based on the evolutionary game theory, this article constructs a quartet evolutionary game model for debt restructuring with the participation of asset management companies; studies the interactive mechanism of complex behaviors among the government, banks, asset management companies, and enterprises; and analyzes the stability of the strategies of each game subject. It also analyzes the stability of the equilibrium points in the system and finds the stable points that maximize the interests of each subject. Research shows that the government chooses to give specific support, banks choose debt-to-equity swaps, asset management companies choose to provide liquidity, and enterprises choose to work hard, which can better promote the debt restructuring process. Finally, using Matlab2018 software to analyze the impact of each essential element in the debt restructuring on the stability of system evolution, the research results provide a basis for the successful debt restructuring of the enterprises.

Virtual reality systems provide realistic look and feel by seamlessly integrating three‐dimensional input and output devices. One software architecture approach to constructing such systems is to distribute the application between a computation‐intensive simulator back‐end and a graphics‐intensive viewer front‐end which implements user interaction. In this paper we discuss Metis, a toolkit we have been developing based on such a software architecture, which can be used for building interactive immersive virtual reality systems with computationally intensive components. The Metis toolkit defines an application programming interface on the simulator side, which communicates via a network with a standalone viewer program that handles all immersive display and interactivity. Network bandwidth and interaction latency are minimized, by use of a constraint network on the viewer side that declaratively defines much of dynamic and interactive behavior of the application.

The accurate prediction of online car-hailing demand plays an increasingly important role in real-time scheduling and dynamic pricing. Most studies have found that the demand of online car-hailing is highly correlated with both temporal and spatial distributions of journeys. However, the importance of temporal and spatial sequences is not distinguished in the context of seeking to improve prediction, when in actual fact different time series and space sequences have different impacts on the distribution of demand and supply for online car-hailing. In order to accurately predict the short-term demand of online car-hailing in different regions of a city, a combined attention-based LSTM (LSTM + Attention) model for forecasting was constructed by extracting temporal features, spatial features, and weather features. Significantly, an attention mechanism is used to distinguish the time series and space sequences of order data. The order data in Haikou city was collected as the training and testing datasets. Compared with other forecasting models (GBDT, BPNN, RNN, and single LSTM), the results show that the short-term demand forecasting model LSTM + Attention outperforms other models. The results verify that the proposed model can support advanced scheduling and dynamic pricing for online car-hailing.

Using mechanism design and dynamic analysis software, the simulation is conducted about grasping operation and moving objects for multi redundant manipulator on the basis of mechanism design and theoretical research, as well as the snake crawling motion simulation. The simulation results validate the correctness of theory analysis. The each joint driving torque data and curve provided the effective data basis for the design and development of a prototype.