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CH-YOLO-Lite: a lightweight object detection model with context-aware progressive aggregation and hierarchical feature aggregation for aerial imagery

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Journal of Electronic Imaging 2025年第2期34卷

作者： Wang, Le Jiang, Wangyu Dharejo, Fayaz Ali Sun, Meng Timofte, Radu Mao, Guojun Fujian University of Technology School of Computer Science and Mathematics Fuzhou China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China Technology Innovation Center of Factored Transaction Data in Tourist Attractions Ministry of Culture and Tourism Fuzhou China University of Wurzburg Computer Vision Lab CAIDAS IFI Wurzburg Germany University of Wurzburg Biocenter Department of Bioinformatics Wurzburg Germany

Object detection in aerial images poses significant challenges due to the high proportion of small objects, drastic scale variations, and resource constraints. We propose a lightweight object detection model, CH-YOLO-Lite, based on context-aware progressive aggregation and hierarchical feature augmentation. First, we designed a context-aware progressive aggregator (CPA) to integrate multi-scale contextual information through a progressive fusion strategy, enhancing the model's perception of its environment and improving small object detection in complex scenes. Second, we designed a hierarchical feature aggregation (HFA) module and an improved adaptive spatial feature fusion (iASFF) method to construct the feature augmentation neck, which significantly enhances the model's multi-scale detection capability in scenarios dominated by small objects. The HFA introduces more low-level detail features into the feature pyramid through cross-level branches, strengthening the model's feature representation for small objects. Meanwhile, iASFF adaptively learns the spatial fusion weights of feature maps at different scales, effectively solving the issue of scale inconsistency during feature fusion, further improving detection accuracy. Finally, to further simplify the model, channel pruning is used, which significantly reduces the parameters of the model while maintaining the detection performance. Experiments on the test set of the VisDrone dataset show that CH-YOLO-Lite significantly improves mAP0.5 by 7.2% and reduces parameters by 67% compared with the baseline model YOLOv7-Tiny. The mAP0.5 of CH-YOLO-Lite is also improved by 4.9% on NWPU VHR-10 datasets, which validates the generalization of our proposed algorithm. © 2025 SPIE and IS&T.

关键词： Aerial photography

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Single-cell technologies: current and near future

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Science China(Life Sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

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Transcriptome analysis reveals PTBP1 as a key regulator of circRNA biogenesis

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BMC Biology 2025年第1期23卷 1-17页

作者： Wang, Mohan Zheng, Shanshan Zhang, Yan Zhang, Jingwen Lai, Fuming Zhou, Cong Zhou, Qiangwei Li, Xingwang Li, Guoliang National Key Laboratory of Crop Genetic Improvement Hubei Hongshan Laboratory Huazhong Agricultural University Wuhan 430070 China Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big Data Key Laboratory of Smart Farming Technology for Agricultural Animals 3D Genomics Research Center College of Informatics Huazhong Agricultural University Wuhan 430070 China

Background: Circular RNAs (circRNAs) are a class of non-coding RNAs generated through back splicing. High expression of circRNAs is often associated with numerous abnormal cellular biological processes. However, the regulatory factors of circRNAs are not fully understood. Results: In this study, we identified PTBP1 as a crucial regulator of circRNA biogenesis through a comprehensive analysis of the whole transcriptome profiles across 10 diverse cell lines. Knockdown of PTBP1 led to a significant decrease in circRNA expression, concomitant with a distinct reduction in cell proliferation. To investigate the regulatory mechanism of PTBP1 on circRNA biogenesis, we constructed a minigene reporter based on SPPL3 gene. The results showed that PTBP1 can bind to the flanking introns of circSPPL3, and the mutation of PTBP1 motif impedes the back splicing of circSPPL3. Subsequently, to demonstrate that this observation is not an exception, the comprehensive regulatory effects of PTBP1 on circRNAs were confirmed by miniGFP, reflecting the necessity of the binding site in the flanking introns. Analysis of data from clinical samples showed that both PTBP1 and circRNAs exhibited substantial upregulation in acute myeloid leukemia, further demonstrating a potential role for PTBP1 in promoting circRNA biogenesis under in vivo conditions. Competitive endogenous RNA (ceRNA) network revealed that PTBP1-associated circRNAs participated in biological processes associated with cell proliferation. Conclusions: In summary, our study is the first to identify the regulatory effect of PTBP1 on circRNA biogenesis and indicates a possible link between PTBP1 and circRNA expression in leukemia. © The Author(s) 2025.

关键词： Biogenesis Circular RNAs PTBP1 RNA binding proteins Transcriptome

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An automatic annotation tool and reference database for T cell subtypes and states at single-cell resolution

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Science Bulletin 2025年第10期70卷 1659-1672页

作者： Shen, Wen-Kang Zhang, Chu-Yu Gu, Yi-Min Luo, Tao Chen, Si-Yi Yue, Tao Xie, Gui-Yan Liao, Yu Yuan, Yong Lei, Qian Guo, An-Yuan Department of Thoracic Surgery West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu610041 China Hubei Bioinformatics & Molecular Imaging Key Laboratory College of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China College of Life Sciences University of Chinese Academy of Sciences Beijing101408 China Department of Rheumatology & Immunology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430074 China

T cells have various subtypes and states with different functions. However, a reference list and automated annotation tool for T cell subtypes and states are lacking, which is critical for analyzing and comparing T cells under various conditions. We constructed the largest human T cell reference, containing 1,348,268 T cells from 35 conditions and 16 tissues. We classified T cells into 33 subtypes and further stratified them into 68 categories according to subtype and state. Based on this reference, we developed a tool named STCAT to automatically annotate T cells from scRNA-seq data by hierarchical models and marker correction. The accuracy of STCAT was 28% higher than that of existing tools validated on six independent datasets, including cancer and healthy samples. Using STCAT, we consistently discovered that CD4+ Th17 cells were enriched in late-stage lung cancer patients in multiple datasets, whereas MAIT cells were prevalent in milder-stage COVID-19 patients. We also confirmed a decrease in Treg cytotoxicity in post-treatment ovarian cancer. Systematic landscape analyses of CD4+ and CD8+ T cell references revealed that CD4+ Treg cells were enriched in tumor samples and that CD8+ naive-related cells were abundant in healthy individuals. Finally, we deposited all the T cell references and annotations into a TCellAtlas (https://***/TCellAtlas) database, which allows users to browse T cell expression profiles and analyze customized scRNA-seq data by STCAT. In conclusion, comprehensive human T cell subtypes and states reference, automated annotation tool, and database will greatly facilitate research on T cell immunity and tumor immunology. © 2025 Science China Press

关键词： T-cells

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Graphical Transformation Models

arXiv

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arXiv 2025年

作者： Herp, Matthias Brachem, Johannes Altenbuchinger, Michael Kneib, Thomas Statistics University of Göttingen Germany Institute Data Science University of Göttingen Campus Germany Department of Medical Bioinformatics University Medical Center Göttingen Germany

Graphical Transformation Models (GTMs) are introduced as a novel approach to effectively model multivariate data with intricate marginals and complex dependency structures non-parametrically, while maintaining interpretability through the identification of varying conditional independencies. GTMs extend multivariate transformation models by replacing the Gaussian copula with a custom-designed multivariate transformation, offering two major advantages. Firstly, GTMs can capture more complex interdependencies using penalized splines, which also provide an efficient regularization scheme. Secondly, we demonstrate how to approximately regularize GTMs using a lasso penalty towards pairwise conditional independencies, akin to Gaussian graphical models. The model’s robustness and effectiveness are validated through simulations, showcasing its ability to accurately learn parametric vine copulas and identify conditional independencies. Additionally, the model is applied to a benchmark astrophysics dataset, where the GTM demonstrates favorable performance compared to non-parametric vine copulas in learning complex multivariate distributions. © 2025, CC BY.

关键词： Astrophysics

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Fine-tuning protein language models to understand the functional impact of missense variants

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Computational and Structural Biotechnology Journal 2025年 27卷 2199-2207页

作者： Ali Saadat Jacques Fellay School of Life Sciences Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland Precision Medicine Unit Biomedical Data Science Center Lausanne University Hospital and University of Lausanne Lausanne Switzerland

Elucidating the functional effects of missense variants is crucial yet challenging. To investigate their impact, we fine-tuned protein language models, including ESM2 and ProtT5, to classify 20 protein features at amino acid resolution. In addition, we trained a fully connected neural network classifier on frozen embeddings and compared its performance to fine-tuning in order to quantify the added value of task-specific adaptation. We then used the fine-tuned models to: 1) identify protein features enriched in either pathogenic or benign missense variants, and 2) compare the predicted feature profiles of proteins with reference and alternate alleles to understand how missense variants affect protein functionality. We show that our models can be used to reclassify variants of uncertain significance and provide mechanistic insights into the functional consequences of missense mutations.

关键词： Missense variant Mechanistic interpretation Protein language models Fine-tuning Token classification

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Single-cell omics: experimental workflow, data analyses and applications

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Science China(Life Sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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The Effect of Multi-Task Learning on the Prediction of Neoantigen–MHC Class II Binding

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and bioinformatics 2025年第2期22卷 570-580页

作者： Kazuhiro Ikkyu Itoshi Nikaido Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Life Science Innovation (Bioinformatics) University of Tsukuba Tsukuba Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Saitama Japan Department of Functional Genome Informatics Division of Biological Data Science Medical Research Laboratory (MRL) Institute of Integrated Research (IIR) Institute of Science Tokyo Bunkyo Japan

Neoepitopes are significant therapeutic cancer vaccine candidates, given that tumor neoepitopes induce an immune response to eliminate cancer cells. This immune activation depends on the binding affinity between the antigen peptide and the major histocompatibility complex (MHC). The epitope–MHC binding assay is a technologically difficult, time-consuming, and expensive technique. Therefore, prediction methods for these binding affinities have been developed using computational prediction approaches. However, these predictive models are trained on datasets biased toward viral peptides and some MHC alleles and are limited in their prediction of neoepitopes. In particular, because of the wide variety of MHC class II binding formats, the performance of MHC class II prediction must be improved. Here, we propose a novel deep learning model that consists of multi-task bidirectional long short-term memory (Bi-LSTM) models. Our multi-task model can predict neoepitope–MHC class II bindings from limited training data by sharing MHC class I and II training parameters. We confirm that multi-task learning significantly enhances the prediction performances of cancer antigens. Our model achieves an area under the receiver operating characteristic curve (AUC-ROC) of 82.2%, outperforming existing state-of-the-art single allele neoantigen prediction models while maintaining its strong generalization performance.

关键词： Peptides Predictive models Multitasking Antigens Cancer data models Training Computer architecture Training data Immune system

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight

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PLoS Computational Biology 2025年第4 April期21卷 e1012999页

作者： Nourbakhsh, Mona Zheng, Yuanning Noor, Humaira Chen, Hongjin Akhuli, Subhayan Tiberti, Matteo Gevaert, Olivier Papaleo, Elena Cancer Systems Biology Section for Bioinformatics Department of Health Technology Technical University of Denmark Lyngby Denmark Cancer Structural Biology Danish Cancer Institute Copenhagen Denmark Department of Biomedical Data Science Stanford Center for Biomedical Informatics Research Palo AltoCA United States

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data. © 2025 Nourbakhsh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, p

关键词： Transcription

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