Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
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。