Segmentation of the AVP using MRI provides important quantitative tools for analyzing AVP morphology and trajectory. However, due to the "X"-shaped structure of the AVP, its elongated morphology located deep within brain tissue, complex anatomical environment, and significant inter-individual morphological differences, traditional morphology- and atlas-based methods struggle to achieve precise segmentation of the AVP, often leading to segmentation failure and limiting clinical application. To address these challenges, deep learning-based AVP segmentation methods have been widely studied. Existing methods face challenges such as loss of texture details, structural discontinuities, and poor generalization, as they fail to accurately extract AVP structures from low-contrast regions and perform global modeling, while also struggling to capture potential periodic patterns in image data, resulting in poor performance on unseen data. In this paper, we propose a global periodic spatiotemporal aware AVP segmentation model named BiFxLSTM-UNet, which is capable of accurately segmenting AVP structures in complex brain environments. Specifically, our model consists of two components: a Fourier Fusion-driven bidirectional xLSTM visual backbone and a UNet decoder. First, the Fourier Fusion-driven bidirectional xLSTM visual backbone captures tight global spatiotemporal relationships between AVP slices, performs global periodic modeling, and learns the periodic patterns underlying the image data. Then, the UNet decoder generates AVP images that closely resemble manual annotations by medical professionals. Extensive comparative experiments demonstrate that, compared to existing methods, our approach shows competitive performance in both qualitative and quantitative evaluations of image quality.

Hemorrhagic transformation (HT) is a time-sensitive severe complication of endovascular thrombectomy for patients with ischemic stroke, and there is an urgent need to develop deep learning models to assist doctors in making rapid preliminary diagnoses. Currently popular Transformer deep learning architectures, while superior in modeling global relationships compared to traditional CNN, it still faces quadratic complexity issues when handling long sequences of medical images due to its inherent attention mechanism. In contrast, computational complexity of the Mamba model-based method grows linearly. Based on these findings, we have developed a novel Mamba model that effectively captures long-range dependencies and the sequential relationships among slices in high-dimensional medical image sequences. We evaluated the proposed model on a multi-center dataset. Experimental results show that our method outperforms other classical architectures and current advanced methods, validating the effectiveness and generalizability of the model composed of the aforementioned modules.

RNA modification, which is evolutionarily conserved, is crucial for modulating various biological functions and disease pathogenesis. High resolution transcriptome-wide mapping of RNA modifications has facilitated both data resources and computational prediction of RNA modification. While these prediction algorithms are promising, they are limited in interpretability or generalizability, or the capacity for discovering novel post-transcriptional regulations. Here, we present NetRNApan, a deep learning framework for RNA modification site prediction, motif discovery and trans-regulatory factor identification. Using m5U profiles generated by FICC-seq and miCLIP-seq technologies and single-base resolution m6A sites from multiple experiments as cases, we demonstrated the accuracy of NetRNApan with more efficient and interpretive feature representations. For m5U modification, we uncovered five representative clusters with consensus motifs that may be essential by decoding the informative characteristics detected by NetRNApan. Furthermore, NetRNApan revealed interesting trans-regulatory factors and provided a protein-binding perspective for investigating the function of RNA modifications. Specifically, we discovered 21 potential functional RNA-binding proteins (RBPs) whose binding sites were significantly linked to the extracted top-scoring motifs for m5U modification. Two examples are ANKHD1 and RBM4 with potential regulatory function of m5U modifications. Meanwhile, the analysis of convolution layer parameters within the model offers valuable insights into the regulation of m6A in humans. Collectively, NetRNApan demonstrated high accuracy, interpretability and generalizability for study of RNA modification and mRNA regulation. NetRNApan is freely available at https://github.com/bsml320/NetRNApan.

Hemorrhagic transformation (HT) is a severe complication of acute ischemic stroke (AIS) that can lead to disability or death. Accurate and timely risk assessment of HT is essential for clinicians to design effective treatment strategies. Previous studies in HT prediction have largely relied on machine learning and radiomics, which demand extensive manual data preprocessing by physicians. While some HT prediction models based on convolutional neural network (CNN) have been developed, they are limited in their ability to capture the sequential relationships between image slices and often lack the focus on crucial information. This study collected non-contrast computed tomography (NCCT) images and clinical data from 512 AIS patients across six hospitals to create a multi-center dataset. Based on the dataset, we propose CM-HTNet, a novel deep learning framework designed to predict HT risk in AIS patients following intravenous thrombolysis (IVT). CM-HTNet mimics the clinical process of reviewing NCCT images by focusing on key slices and integrating information from adjacent slices. It leverages CNNs to extract features from each NCCT slice and utilizes the Selective State-Space Model (SSM) within the Mamba framework to model sequential relationships between slices while prioritizing features relevant to HT prediction. Additionally, CM-HTNet incorporates the Neighborhood Rough Set (KRS) algorithm for clinical feature selection and cross-attention mechanisms to integrate clinical and imaging data for multimodal HT risk prediction. In testing on an external dataset from independent centers, CM-HTNet achieved a prediction accuracy of 88.85% and an AUC of 95.17%, showcasing its strong performance and generalization capabilities.

Protein degradation through the ubiquitin proteasome system at the spatial and temporal regulation is essential for many cellular processes. E3 ligases and degradation signals (degrons), the sequences they recognize in the target proteins, are key parts of the ubiquitin-mediated proteolysis, and their interactions determine the degradation specificity and maintain cellular homeostasis. To date, only a limited number of targeted degron instances have been identified, and their properties are not yet fully characterized. To tackle on this challenge, here we develop a novel deep-learning framework, namely MetaDegron, for predicting E3 ligase targeted degron by integrating the protein language model and comprehensive featurization strategies. Through extensive evaluations using benchmark datasets and comparison with existing method, such as Degpred, we demonstrate the superior performance of MetaDegron. Among functional features, MetaDegron allows batch prediction of targeted degrons of 21 E3 ligases, and provides functional annotations and visualization of multiple degron-related structural and physicochemical features. MetaDegron is freely available at http://modinfor.com/MetaDegron/. We anticipate that MetaDegron will serve as a useful tool for the clinical and translational community to elucidate the mechanisms of regulation of protein homeostasis, cancer research, and drug development.

As a hot topic in recent years, the ability of pedestrians identification based on radar micro-Doppler signatures is limited by the lack of adequate training data. In this paper, we propose a data-enhanced multi-characteristic learning (DEMCL) model with data enhancement (DE) module and multi-characteristic learning (MCL) module to learn more complementary pedestrian micro-Doppler (m-D) signatures. In DE module, a range-Doppler generative adversarial network (RDGAN) is proposed to enhance free walking datasets, and MCL module with multi-scale convolution neural network (MCNN) and radial basis function neural network (RBFNN) is trained to learn m- D signatures extracted from enhanced datasets. Experimental results show that our model is 3.33% to 10.24% more accurate than other studies and has a short run time of 0.9324 seconds on a 25-minute walking dataset.