The influence of online Danmu on users\u27 reward behavior: Based on the data of Douyu live broadcast

In live streaming, the Danmu is a crucial technique of interaction, and the reward is the interaction\u27s feedback. The audience receives more input through the reward the more frequently they interact. The effect of the bullet screen in the live broadcast on the audience\u27s reward behavior was investigated by gathering data from the live broadcast room 5720533 on Douyu, a domestic Danmu live-streaming website, from February 14 to February 24, 2021. Based on empirical research, the following conclusions can be drawn: the number of user Danmu, the proportion of fan Danmu, the number of user entry Danmu, and the number of super Danmu will all significantly improve users\u27 reward, while personal experience attenuates the positive impact of the number of user access Danmu and the number of super Danmu on the impact of user reward. The study\u27s findings will offer theoretical justification for the creation of live broadcast platforms, the upkeep of anchors\u27 notoriety, and users\u27 rational consumption

A capsule network-based method for identifying transcription factors

Transcription factors (TFs) are typical regulators for gene expression and play versatile roles in cellular processes. Since it is time-consuming, costly, and labor-intensive to detect it by using physical methods, it is desired to develop a computational method to detect TFs. Here, we presented a capsule network-based method for identifying TFs. This method is an end-to-end deep learning method, consisting mainly of an embedding layer, bidirectional long short-term memory (LSTM) layer, capsule network layer, and three fully connected layers. The presented method obtained an accuracy of 0.8820, being superior to the state-of-the-art methods. These empirical experiments showed that the inclusion of the capsule network promoted great performances and that the capsule network-based representation was superior to the property-based representation for distinguishing between TFs and non-TFs. We also implemented the presented method into a user-friendly web server, which is freely available at http://www.biolscience.cn/Capsule_TF/ for all scientific researchers

First-Principles Investigation of Phase Stability, Electronic Structure and Optical Properties of MgZnO Monolayer

MgZnO bulk has attracted much attention as candidates for application in optoelectronic devices in the blue and ultraviolet region. However, there has been no reported study regarding two-dimensional MgZnO monolayer in spite of its unique properties due to quantum confinement effect. Here, using density functional theory calculations, we investigated the phase stability, electronic structure and optical properties of MgxZn1−xO monolayer with Mg concentration x range from 0 to 1. Our calculations show that MgZnO monolayer remains the graphene-like structure with various Mg concentrations. The phase segregation occurring in bulk systems has not been observed in the monolayer due to size effect, which is advantageous for application. Moreover, MgZnO monolayer exhibits interesting tuning of electronic structure and optical properties with Mg concentration. The band gap increases with increasing Mg concentration. More interestingly, a direct to indirect band gap transition is observed for MgZnO monolayer when Mg concentration is higher than 75 at %. We also predict that Mg doping leads to a blue shift of the optical absorption peaks. Our results may provide guidance for designing the growth process and potential application of MgZnO monolayer

MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction

Abstract N4-methylcytosine (4mC) is an important epigenetic mechanism, which regulates many cellular processes such as cell differentiation and gene expression. The knowledge about the 4mC sites is a key foundation to exploring its roles. Due to the limitation of techniques, precise detection of 4mC is still a challenging task. In this paper, we presented a multi-scale convolution neural network (CNN) and adaptive embedding-based computational method for predicting 4mC sites in mouse genome, which was referred to as MultiScale-CNN-4mCPred. The MultiScale-CNN-4mCPred used adaptive embedding to encode nucleotides, and then utilized multi-scale CNNs as well as long short-term memory to extract more in-depth local properties and contextual semantics in the sequences. The MultiScale-CNN-4mCPred is an end-to-end learning method, which requires no sophisticated feature design. The MultiScale-CNN-4mCPred reached an accuracy of 81.66% in the 10-fold cross-validation, and an accuracy of 84.69% in the independent test, outperforming state-of-the-art methods. We implemented the proposed method into a user-friendly web application which is freely available at: http://www.biolscience.cn/MultiScale-CNN-4mCPred/

MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides

Bioactive peptides are typically small functional peptides with 2–20 amino acid residues and play versatile roles in metabolic and biological processes. Bioactive peptides are multi-functional, so it is vastly challenging to accurately detect all their functions simultaneously. We proposed a convolution neural network (CNN) and bi-directional long short-term memory (Bi-LSTM)-based deep learning method (called MPMABP) for recognizing multi-activities of bioactive peptides. The MPMABP stacked five CNNs at different scales, and used the residual network to preserve the information from loss. The empirical results showed that the MPMABP is superior to the state-of-the-art methods. Analysis on the distribution of amino acids indicated that the lysine preferred to appear in the anti-cancer peptide, the leucine in the anti-diabetic peptide, and the proline in the anti-hypertensive peptide. The method and analysis are beneficial to recognize multi-activities of bioactive peptides

PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences

RNA–protein interactions play an indispensable role in many biological processes. Growing evidence has indicated that aberration of the RNA–protein interaction is associated with many serious human diseases. The precise and quick detection of RNA–protein interactions is crucial to finding new functions and to uncovering the mechanism of interactions. Although many methods have been presented to recognize RNA-binding sites, there is much room left for the improvement of predictive accuracy. We present a sequence semantics-based method (called PRIP) for predicting RNA-binding interfaces. The PRIP extracted semantic embedding by pre-training the Word2vec with the corpus. Extreme gradient boosting was employed to train a classifier. The PRIP obtained a SN of 0.73 over the five-fold cross validation and a SN of 0.67 over the independent test, outperforming the state-of-the-art methods. Compared with other methods, this PRIP learned the hidden relations between words in the context. The analysis of the semantics relationship implied that the semantics of some words were specific to RNA-binding interfaces. This method is helpful to explore the mechanism of RNA–protein interactions from a semantics point of view

Al-Doped ZnO Monolayer as a Promising Transparent Electrode Material: A First-Principles Study

Al-doped ZnO has attracted much attention as a transparent electrode. The graphene-like ZnO monolayer as a two-dimensional nanostructure material shows exceptional properties compared to bulk ZnO. Here, through first-principle calculations, we found that the transparency in the visible light region of Al-doped ZnO monolayer is significantly enhanced compared to the bulk counterpart. In particular, the 12.5 at% Al-doped ZnO monolayer exhibits the highest visible transmittance of above 99%. Further, the electrical conductivity of the ZnO monolayer is enhanced as a result of Al doping, which also occurred in the bulk system. Our results suggest that Al-doped ZnO monolayer is a promising transparent conducting electrode for nanoscale optoelectronic device applications