Protein-RNA complexes, particularly those involving RNA-binding proteins and long non-coding RNAs (lncRNA), are commonly found to influence gene expression and mediate fundamental cellular processes. Despite significant advances in representations for these biological sequences, sequence decomposition based on k-mer generally results in fix-length substrings, failing to detect the information of variable-length biological functional regions. In this paper, we develop a concept of expressiveness for k-mer decompositions as a theoretical underpinning for traversing all k-mer decompositions. Based on this concept, we propose an advanced approach, BERTDGA-LPI, to detect the information of variable-length biological functional regions utilizing dynamic graph attention and to capture the influence of RNA and protein context leveraging pretrained language models. The experimental results demonstrate the outperformance of BERTDGA-LPI over state-of-the-art methods across two homo sapiens datasets, one plant species dataset, and two species-unspecific datasets. Furthermore, BERTDGA-LPI is validated as effective in predicting unknown RNA-protein interactions (RPI) with 100% prediction accuracy in six independent validation sets from different species. This study lays a theoretical underpinning for traversing all k-mer decompositions and innovatively offers a broadly applicable and efficient tool for LPI prediction and RPI prediction based only on sequences.

The goal of conditional image-to-video (cI2V) generation is to create a believable new video by beginning with the condition, i.e., one image and text. The previous cI2V generation methods conventionally perform in RGB pixel space, with limitations in modeling motion consistency and visual continuity. Additionally, the efficiency of generating videos in pixel space is quite low. In this paper, we propose a novel approach to address these challenges by disentangling the target RGB pixels into two distinct components: spatial content and temporal motions. Specifically, we predict temporal motions which include motion vector and residual based on a 3D-UNet diffusion model. By explicitly modeling temporal motions and warping them to the starting image, we improve the temporal consistency of generated videos. This results in a reduction of spatial redundancy, emphasizing temporal details. Our proposed method achieves performance improvements by disentangling content and motion, all without introducing new structural complexities to the model. Extensive experiments on various datasets confirm our approach's superior performance over the majority of state-of-the-art methods in both effectiveness and efficiency.

In this article, a robust distributed fault detection and isolation (FDI) scheme for second-order multiagent systems (MASs) with exogenous disturbances based on partial agents' absolute state will be proposed. We design a robust control law with disturbance rejection term for each agent, then, an augmented MAS closed-loop system without disturbance term is formed. We construct a bank of observers for FDI in some selected agents for the augmented MAS closed-loop system based on the idea of an unknown input observer (UIO), where the observers for FDI of the entire MAS are updated by the selected agents' absolute states and relative states between themselves and their neighbors, which avoids utilizing all agents' absolute states. We run observers for FDI in the selected agents to generate residuals to detect the faulty agent between themselves and their neighbors. Meanwhile, the existence proof of observers for FDI is also presented. It is worth noting that the remaining healthy agents will reach consensus again under the original robust control law if the communication topology is still connected. Finally, the effectiveness of the proposed scheme is illustrated by a multivehicle example.