Paper List
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STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
This paper addresses the core challenge of generalizing protein function prediction to unseen or newly introduced Gene Ontology (GO) terms by overcomi...
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Incorporating indel channels into average-case analysis of seed-chain-extend
This paper addresses the core pain point of bridging the theoretical gap for the widely used seed-chain-extend heuristic by providing the first rigoro...
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Competition, stability, and functionality in excitatory-inhibitory neural circuits
This paper addresses the core challenge of extending interpretable energy-based frameworks to biologically realistic asymmetric neural networks, where...
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Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4
This paper addresses the core challenge of generating accurate and clinically relevant patient notes from sparse inputs (ICD codes and basic demograph...
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Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited his...
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Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring ...
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Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study
This study addresses the critical gap in understanding heterogeneous contact patterns within aged care facilities, where existing population-level con...
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
使用QuPath和OMERO进行全切片和显微镜图像分析
Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
30秒速读
IN SHORT: 使QuPath能够直接分析存储在OMERO服务器中的图像而无需下载整个数据集，克服了大规模研究的本地存储限制。
核心创新
- Methodology Developed a new QuPath extension from scratch with three pixel access APIs (web, Ice, pixel data microservice) for flexible server compatibility
- Methodology Implemented unit testing with automated Docker container creation for OMERO server connections, ensuring software reliability
- Methodology Designed modular architecture separating core logic (headless operation) from GUI components, improving maintainability and scriptability
主要结论
- 自2024年2月以来，该扩展已被下载29,727次，显示了广泛的采用和活跃的用户社区。
- 支持高达20 GB未压缩的全切片图像（120,000 x 60,000像素）以及具有数十个16位/32位通道的荧光多重图像。
- 通过OMERO集成，能够访问包含超过400 TB已发布成像数据的IDR存储库。
摘要: QuPath是用于生物图像分析的开源软件。作为一个灵活且易于安装的桌面应用程序，QuPath被全球实验室用于可视化和分析大型复杂图像。然而，仅依赖存储在本地文件系统上的图像限制了QuPath在更大规模研究中的应用。本文描述了一个新的扩展，使QuPath能够从OMERO服务器访问像素和元数据。这通过允许软件高效处理远程存储的图像来增强其功能，同时也为希望将QuPath连接到其他图像管理系统的开发人员提供了模板。