通过算法图像处理和基于深度学习的技术在三维冷冻电子断层扫描/电子显微镜（cryo-ET/EM）密度图中追踪与分割分子模式
Tracing and segmentation of molecular patterns in 3-dimensional cryo-et/em density maps through algorithmic image processing and deep learning-based techniques
AI总结：
本文针对cryo-ET/EM图像质量差等挑战，开发了BundleTrac、Spaghetti Tracer和Struwwel Tracer三种肌动蛋白丝追踪方法，并提出基于CNN的DeepSSETracer分割模型和圆柱体拟合度量算法，为结构生物学提供了重要计算工具。
摘要：
理解生物大分子的结构非常重要，因为它们与细胞功能密切相关。理解肌动蛋白丝的精确组织至关重要，因为它们构成了动态细胞骨架，为细胞提供结构支持，并将细胞内部与其周围环境连接起来。然而，由于冷冻电子断层扫描（cryo-ET）图像质量较差，存在低信噪比（SNR）和缺失楔形伪影问题，加上肌动蛋白丝具有多样的形状特征，确定其精确组织极具挑战性。为应对这些艰巨挑战，本论文的核心部分致力于开发用于追踪肌动蛋白丝的复杂计算技术。具体而言，我们开发了三种新方法：i) BundleTrac，用于追踪Stereocilium中发现的束状肌动蛋白丝；ii) Spaghetti Tracer，用于追踪以松散内聚方式单独移动的丝状物；iii) Struwwel Tracer，用于追踪肌动蛋白网络中随机取向的肌动蛋白丝。论文的第二部分介绍了一种基于卷积神经网络（CNN）的分割模型，用于确定中等分辨率（5-10埃）三维冷冻电子显微镜（cryo-EM）图像中蛋白质二级结构（如螺旋和β折叠片）的位置。该方法后来演变为名为DeepSSETracer的工具。论文的最后一部分提出了一种新算法，即圆柱体拟合度量，用于评估中等分辨率cryo-EM图像中螺旋区域的图像结构匹配度。总体而言，我的论文通过引入多种计算方法和工具，为解决结构生物学中的关键研究挑战做出了重大贡献。
英文摘要：
Understanding the structures of biological macromolecules is highly important as they are closely associated with cellular functionalities. Comprehending the precise organization actin filaments is crucial because they form the dynamic cytoskeleton, which offers structural support to cells and connects the cell's interior with its surroundings. However, determining the precise organization of actin filaments is challenging due to the poor quality of cryo-electron tomography (cryo-ET) images, which suffer from low signal-to-noise (SNR) ratios and the presence of missing wedge, as well as diverse shape characteristics of actin filaments. To address these formidable challenges, the primary component of this dissertation focuses on developing sophisticated computational techniques for tracing actin filaments. In particular, three novel methodologies have been developed: i) BundleTrac, for tracing bundle-like actin filaments found in Stereocilium, ii) Spaghetti Tracer, for tracing filaments that move individually with loosely cohesive movements, and iii) Struwwel Tracer, for tracing randomly orientated actin filaments in the actin network. The second component of the dissertation introduces a convolutional neural network (CNN) based segmentation model to determine the location of protein secondary structures, such as helices and beta-sheets, in medium-resolution (5-10 Angstrom) 3-dimensional cryo-electron microscopy (cryo-EM) images. This methodology later evolved into a tool named DeepSSETracer. The final component of the dissertation presents a novel algorithm, cylindrical fit measure, to estimate image structure match at helix regions in medium-resolution cryo-EM images. Overall, my dissertation has made significant contributions to addressing critical research challenges in structural biology by introducing various computational methods and tools.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
