Yutong Guo

Kewei Li, Yuqian Wu, Yinheng Li et al.

Since the mechanism of action of drug molecules in the human body is difficult to reproduce in the in vitro environment, it becomes difficult to reveal the causes of the activity cliff phenomenon of drug molecules. We found out the AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in peptides with canonical amino acids. Understanding the mechanism of AC in canonical amino acids might help understand the one in drug molecules. This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the MIC, and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the MIC values on the benchmark dataset. Source code and additional resources are available at https://www.healthinformaticslab.org/supp/ or https://github.com/Kewei2023/AMPCliff-generation.

Yutong Guo

Modern video super-resolution (VSR) systems based on convolutional neural networks (CNNs) require huge computational costs. The problem of feature redundancy is present in most models in many domains, but is rarely discussed in VSR. We experimentally observe that many features in VSR models are also similar to each other, so we propose to use "Ghost features" to reduce this redundancy. We also analyze the so-called "gradient disappearance" phenomenon generated by the conventional recurrent convolutional network (RNN) model, and combine the Ghost module with RNN to complete the modeling on time series. The current frame is used as input to the model together with the next frame, the output of the previous frame and the hidden state. Extensive experiments on several benchmark models and datasets show that the PSNR and SSIM of our proposed modality are improved to some extent. Some texture details in the video are also better preserved.

Jixuan He, Yutong Guo, Jiacheng Zhao

This paper delves into the paramount significance of information dissemination within the dynamic realm of social networks. It underscores the pivotal role of information communication models in unraveling the intricacies of data propagation in the digital age. By shedding light on the profound influence of these models, it not only lays the groundwork for exploring various hierarchies and their manifestations but also serves as a catalyst for further research in this formidable field.