Paper List
-
Translating Measures onto Mechanisms: The Cognitive Relevance of Higher-Order Information
This review addresses the core challenge of translating abstract higher-order information theory metrics (e.g., synergy, redundancy) into defensible, ...
-
Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs
This paper addresses the critical gap in understanding whether LLMs spontaneously develop human-like Bayesian strategies for processing uncertain info...
-
Vessel Network Topology in Molecular Communication: Insights from Experiments and Theory
This work addresses the critical lack of experimentally validated channel models for molecular communication within complex vessel networks, which is ...
-
Modulation of DNA rheology by a transcription factor that forms aging microgels
This work addresses the fundamental question of how the transcription factor NANOG, essential for embryonic stem cell pluripotency, physically regulat...
-
Imperfect molecular detection renormalizes apparent kinetic rates in stochastic gene regulatory networks
This paper addresses the core challenge of distinguishing genuine stochastic dynamics of gene regulatory networks from artifacts introduced by imperfe...
-
PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer
This paper addresses the dual challenge of achieving computational efficiency without sacrificing accuracy in whole-transcriptome single-cell represen...
-
Beyond Bayesian Inference: The Correlation Integral Likelihood Framework and Gradient Flow Methods for Deterministic Sampling
This paper addresses the core challenge of calibrating complex biological models (e.g., PDEs, agent-based models) with incomplete, noisy, or heterogen...
-
Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
This paper addresses the core challenge of detecting viral variants in wastewater sequencing data without reference genomes or labeled annotations, ov...
SHREC: A Spectral Embedding-Based Approach for Ab-Initio Reconstruction of Helical Molecules
Unknown
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.