MOTIVATION: Cryo-electron microscopy (Cryo-EM) single particle analysis (SPA) is a key technique for revealing the structure of biomacromolecules by three-dimensional reconstruction. Achieving high-resolution reconstruction relies on the acquisition of a large number of authentic particles; however, manual particle picking is inefficient and inadequate for the demands of reconstruction, making automated particle picking a major research focus. Although the foundational segmentation model Segment Anything Model (SAM) has recently advanced automated particle picking, its segmentation advantages have not been fully realized in cryo-EM applications. Moreover, cryo-EM images often have significant noise. Conventional denoising decreases noise but frequently overlooks high-level semantic information, leading to oversmoothed particle regions and reduced particle distinguishability. RESULTS: To address these challenges, we propose CryoPromptSeg, which employs prompt-guided SAM for particle picking while integrating a semantically enhanced image denoiser. Specifically, by performing domain adaptation fine-tuning of SAM and incorporating prompts generated by the proposed automatic prompt generator, it achieves precise segmentation of cryo-EM particles. In addition, it employs a parallel multi-task framework to jointly train the denoiser and the prompt generator, incorporating particle semantic information from the prompt generator into the denoiser to suppress noise while preserving highly distinguishable particle structures. To lower the barrier to practical application, we developed a user-friendly online prediction platform for particle picking. Experimental results demonstrate that CryoPromptSeg outperforms existing mainstream methods in both particle picking accuracy and image denoising quality, thus providing a novel solution for the automation of particle picking. AVAILABILITY: The code and platform are available at: https://github.com/347251369/CryoPromptSeg.

Abstract Backround Cell annotation is a key step for characterizing cellular heterogeneity using scRNA-seq data. The accuracy of cell annotation directly affects the reliability of downstream studies. However, annotating cells by manually tools is cumbersome and time-consuming, which is hard to meet the requests for scRNA-seq data analysis. Methods To address this issue, we introduce an LLM-driven agent framework AnnoAgent by integrating mainstream cell annotation models, which can achieve high-performance automatic annotation of cell types by playing strategist and assessor roles. Firstly, strategist will receive reference dataset and automatically performs data processing to generate training and validation matrices. Secondly, strategist employs training and validation matrices to train four annotation models, scBiGNN, CellTypist [1], SVM and scVI [2] as the implementer. Thirdly, strategist invokes implementer to predict the cell types in query dataset, and then generates metrics for each implementer. Finally, the assessor evaluates results of the comprehensive metrics and provide the final predicted cell types. Results The evaluation results on the lung adenocarcinoma scRNA-seq dataset [3] indicate that AnnoAgent can integrate multiple mainstream cell annotation models to achieve optimal annotation performance, which not only greatly reduces the workload of scientific data analysis but also offers a new solution for precise automated annotation of scRNA-seq data. References 1. Domínguez, C. ‘Cross-tissue immune cell analysis reveals tissue-specific features in humans.’ Science, 2022;376(6594):p.eabl5197. 2. Virshup, I. ‘The scverse project provides a computational ecosystem for single-cell omics data analysis.’ Nature Biotechnology, 2023;41(5):p.604–606. 3. Huang, H. ‘S100a4+ alveolar macrophages accelerate the progression of precancerous atypical adenomatous hyperplasia by promoting the angiogenic function regulated by fatty acid metabolism.’ eLife, 2025;13:p. RP101731.

Reachability queries ask whether a vertex can reach another vertex on large directed graphs. It is one of the most fundamental graph operators and has attracted researchers in both academics and industry to study it. The main technical challenge is to support fast reachability queries by efficient managing the three main costs: the index construction time, the index size and the query processing time on large/small and sparse/dense graphs. As real world graphs grow bigger in size, these problems remain open challenges that demand high performance solutions. In this article, we propose aMulti-Dimensional Graph Labelingapproach (calledMGTag) to supporting fast reachability queries.MGTagis novel in three aspects. First, it recursively partitions a graph into multiple subgraphs with disjoint vertex sets, called non-shared graphs, and several inter-partition edges, called cross-edges. Second, we build a four-dimensional label – one dimension of layer, one dimension of non-shared graph and two dimensions of interval for each vertex in non-shared graphs. Finally, with the four-dimensional labeling scheme, we design algorithms to answer reachability queries efficiently. The extensive experiments on 28 large/small and dense/sparse graphs show that building the high dimensional index is quickly and the index size is also competitive compared with most of the state-of-the-art approaches. The results also show that our approach is more scalable and efficient than the state-of-the-art approaches in answering reachability queries on large/small and sparse/dense graphs.

BACKGROUND: Glioblastoma multiforme (GBM) is one of the most common malignant brain tumors and its average survival time is less than 1 year after diagnosis. RESULTS: Firstly, this study aims to develop the novel survival analysis algorithms to explore the key genes and proteins related to GBM. Then, we explore the significant correlation between AEBP1 upregulation and increased EGFR expression in primary glioma, and employ a glioma cell line LN229 to identify relevant proteins and molecular pathways through protein network analysis. Finally, we identify that AEBP1 exerts its tumor-promoting effects by mainly activating mTOR pathway in Glioma. CONCLUSIONS: We summarize the whole process of the experiment and discuss how to expand our experiment in the future.