Paper List
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Macroscopic Dominance from Microscopic Extremes: Symmetry Breaking in Spatial Competition
This paper addresses the fundamental question of how microscopic stochastic advantages in spatial exploration translate into macroscopic resource domi...
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Linear Readout of Neural Manifolds with Continuous Variables
This paper addresses the core challenge of quantifying how the geometric structure of high-dimensional neural population activity (neural manifolds) d...
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Theory of Cell Body Lensing and Phototaxis Sign Reversal in “Eyeless” Mutants of Chlamydomonas
This paper solves the core puzzle of how eyeless mutants of Chlamydomonas exhibit reversed phototaxis by quantitatively modeling the competition betwe...
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Cross-Species Transfer Learning for Electrophysiology-to-Transcriptomics Mapping in Cortical GABAergic Interneurons
This paper addresses the challenge of predicting transcriptomic identity from electrophysiological recordings in human cortical interneurons, where li...
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Uncovering statistical structure in large-scale neural activity with Restricted Boltzmann Machines
This paper addresses the core challenge of modeling large-scale neural population activity (1500-2000 neurons) with interpretable higher-order interac...
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Realizing Common Random Numbers: Event-Keyed Hashing for Causally Valid Stochastic Models
This paper addresses the critical problem that standard stateful PRNG implementations in agent-based models violate causal validity by making random d...
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A Standardized Framework for Evaluating Gene Expression Generative Models
This paper addresses the critical lack of standardized evaluation protocols for single-cell gene expression generative models, where inconsistent metr...
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Single Molecule Localization Microscopy Challenge: A Biologically Inspired Benchmark for Long-Sequence Modeling
This paper addresses the core challenge of evaluating state-space models on biologically realistic, sparse, and stochastic temporal processes, which a...
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。