Paper List
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Mapping of Lesion Images to Somatic Mutations
This paper addresses the critical bottleneck of delayed genetic analysis in cancer diagnosis by predicting a patient's full somatic mutation profile d...
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Reinventing Clinical Dialogue: Agentic Paradigms for LLM‑Enabled Healthcare Communication
This paper addresses the core challenge of transforming reactive, stateless LLMs into autonomous, reliable clinical dialogue agents capable of longitu...
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Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization
通过将序列映射到二元潜在空间进行基于QUBO的适应度优化,桥接蛋白质表示学习和组合优化。
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Controlling Fish Schools via Reinforcement Learning of Virtual Fish Movement
证明了无模型强化学习可以利用虚拟视觉刺激有效引导鱼群,克服了缺乏精确行为模型的问题。
SHREC: A Spectral Embedding-Based Approach for Ab-Initio Reconstruction of Helical Molecules
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30秒速读
IN SHORT: This paper addresses the core bottleneck in cryo-EM helical reconstruction: eliminating the dependency on accurate initial symmetry parameter estimation, which is traditionally obtained through error-prone trial-and-error or prior knowledge.
核心创新
- Methodology Introduces SHREC, the first algorithm that directly recovers projection angles from 2D cryo-EM images without requiring prior knowledge of helical symmetry parameters (rise, twist, or pitch).
- Methodology Leverages the mathematical insight that projections of helical segments form a one-dimensional manifold, enabling recovery through spectral embedding techniques.
- Methodology Requires only knowledge of the specimen's axial symmetry group (Cn), significantly reducing the prior information needed compared to traditional methods.
主要结论
- SHREC successfully recovers projection angles and helical parameters directly from 2D images, validated on public datasets, achieving high-resolution reconstructions.
- The method is proven mathematically: projections of helical segments form a 1D manifold (Lemma 1.9), and the angle between segments θ is directly related to their axial displacement (θ = 2π/P * (t2 - t1), Lemma 1.6).
- By eliminating the initial symmetry estimation step, SHREC provides a more robust and automated pathway, reducing a major source of error in ab-initio helical reconstruction.
摘要: Cryo-electron microscopy (cryo-EM) has emerged as a powerful technique for determining the three-dimensional structures of biological molecules at near-atomic resolution. However, reconstructing helical assemblies presents unique challenges due to their inherent symmetry and the need to determine unknown helical symmetry parameters. Traditional approaches require an accurate initial estimation of these parameters, which is often obtained through trial and error or prior knowledge. These requirements can lead to incorrect reconstructions, limiting the reliability of ab initio helical reconstruction. In this work, we present SHREC (Spectral Helical REConstruction), an algorithm that directly recovers the projection angles of helical segments from their two-dimensional cryo-EM images, without requiring prior knowledge of helical symmetry parameters. Our approach leverages the insight that projections of helical segments form a one-dimensional manifold, which can be recovered using spectral embedding techniques. Experimental validation on publicly available datasets demonstrates that SHREC achieves high resolution reconstructions while accurately recovering helical parameters, requiring only knowledge of the specimen’s axial symmetry group. By eliminating the need for initial symmetry estimates, SHREC offers a more robust and automated pathway for determining helical structures in cryo-EM.