scholarly journals A high-density 3D localization algorithm for stochastic optical reconstruction microscopy

2012 ◽  
Vol 1 (1) ◽  
pp. 6 ◽  
Author(s):  
Hazen Babcock ◽  
Yaron M Sigal ◽  
Xiaowei Zhuang
Keyword(s):  
High Density ◽  
Localization Algorithm ◽  
3D Localization ◽  
Stochastic Optical Reconstruction Microscopy ◽  
Optical Reconstruction

scholarly journals Blinking statistics and molecular counting in direct stochastic reconstruction microscopy (dSTORM)

2021 ◽  
Author(s):  
Lekha Patel ◽  
David Williamson ◽  
Dylan M Owen ◽  
Edward A K Cohen
Keyword(s):  
Probability Distribution ◽  
Single Molecule ◽  
Immunological Synapse ◽  
Training Data ◽  
Supplementary Information ◽  
Cellular Structures ◽  
Exact Probability ◽  
Stochastic Optical Reconstruction Microscopy ◽  
Exact Probability Distribution ◽  
Optical Reconstruction

Abstract Motivation Many recent advancements in single-molecule localization microscopy exploit the stochastic photoswitching of fluorophores to reveal complex cellular structures beyond the classical diffraction limit. However, this same stochasticity makes counting the number of molecules to high precision extremely challenging, preventing key insight into the cellular structures and processes under observation. Results Modelling the photoswitching behaviour of a fluorophore as an unobserved continuous time Markov process transitioning between a single fluorescent and multiple dark states, and fully mitigating for missed blinks and false positives, we present a method for computing the exact probability distribution for the number of observed localizations from a single photoswitching fluorophore. This is then extended to provide the probability distribution for the number of localizations in a direct stochastic optical reconstruction microscopy experiment involving an arbitrary number of molecules. We demonstrate that when training data are available to estimate photoswitching rates, the unknown number of molecules can be accurately recovered from the posterior mode of the number of molecules given the number of localizations. Finally, we demonstrate the method on experimental data by quantifying the number of adapter protein linker for activation of T cells on the cell surface of the T-cell immunological synapse. Availability and implementation Software and data available at https://github.com/lp1611/mol_count_dstorm. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Molecular organization of mammalian meiotic chromosome axis revealed by expansion STORM microscopy

2019 ◽  
Vol 116 (37) ◽  
pp. 18423-18428 ◽  
Author(s):  
Huizhong Xu ◽  
Zhisong Tong ◽  
Qing Ye ◽  
Tengqian Sun ◽  
Zhenmin Hong ◽  
...  
Keyword(s):  
Meiotic Chromosome ◽  
Molecular Organization ◽  
Homologous Chromosomes ◽  
Meiotic Chromosomes ◽  
C Terminus ◽  
Stochastic Optical Reconstruction Microscopy ◽  
Optical Reconstruction ◽  
20 Nm ◽  
Chromosome Axis

During prophase I of meiosis, chromosomes become organized as loop arrays around the proteinaceous chromosome axis. As homologous chromosomes physically pair and recombine, the chromosome axis is integrated into the tripartite synaptonemal complex (SC) as this structure’s lateral elements (LEs). While the components of the mammalian chromosome axis/LE—including meiosis-specific cohesin complexes, the axial element proteins SYCP3 and SYCP2, and the HORMA domain proteins HORMAD1 and HORMAD2—are known, the molecular organization of these components within the axis is poorly understood. Here, using expansion microscopy coupled with 2-color stochastic optical reconstruction microscopy (STORM) imaging (ExSTORM), we address these issues in mouse spermatocytes at a resolution of 10 to 20 nm. Our data show that SYCP3 and the SYCP2 C terminus, which are known to form filaments in vitro, form a compact core around which cohesin complexes, HORMADs, and the N terminus of SYCP2 are arrayed. Overall, our study provides a detailed structural view of the meiotic chromosome axis, a key organizational and regulatory component of meiotic chromosomes.

scholarly journals Mapping Neuronal Connectivity Using Stochastic Optical Reconstruction Microscopy (Storm): The Brainstorm Project

2010 ◽  
Vol 98 (3) ◽  
pp. 214a
Author(s):  
Melike Lakadamyali ◽  
Mark Bates ◽  
Hazen Babcock ◽  
Jeff Lichtman ◽  
Xiaowei Zhuang
Keyword(s):  
Neuronal Connectivity ◽  
Stochastic Optical Reconstruction Microscopy ◽  
Optical Reconstruction

scholarly journals Quenched Stochastic Optical Reconstruction Microscopy (qSTORM) with Graphene Oxide

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Ruiheng Li ◽  
Pantelis Georgiades ◽  
Henry Cox ◽  
Sorasak Phanphak ◽  
Ian S. Roberts ◽  
...  
Keyword(s):  
Graphene Oxide ◽  
Stochastic Optical Reconstruction Microscopy ◽  
Optical Reconstruction

scholarly journals Secure and Robust 3D Localization in Wireless Sensor Networks

2019 ◽  
Vol 9 (1S) ◽  
pp. 104-111
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Malicious Nodes ◽  
3D Localization ◽  
Secure Localization ◽  
Simulation Results ◽  
Two Phases

The fundamental capacity of a sensor system is to accumulate and forward data to the destination. It is crucial to consider the area of gathered data, which is utilized to sort information that can be procured using confinement strategy as a piece of Wireless Sensor Networks (WSNs).Localization is a champion among the most basic progressions since it agreed as an essential part in various applications, e.g., target tracking. If the client can't gain the definite area information, the related applications can't be skillful. The crucial idea in most localization procedures is that some deployed nodes with known positions (e.g., GPS-equipped nodes) transmit signals with their coordinates so as to support other nodes to localize themselves. This paper mainly focuses on the algorithm that has been proposed to securely and robustly decide thelocation of a sensor node. The algorithm works in two phases namely Secure localization phase and Robust Localization phase. By "secure", we imply that malicious nodes should not effectively affect the accuracy of the localized nodes. By “robust”, we indicate that the algorithm works in a 3D environment even in the presence of malicious beacon nodes. The existing methodologies were proposed based on 2D localization; however in this work in addition to security and robustness, exact localization can be determined for 3D areas by utilizing anefficient localization algorithm. Simulation results exhibit that when compared to other existing algorithms, our proposed work performs better in terms of localization error and accuracy.

A High Density WSN Cluster Positioning Approach

2021 ◽  
Vol 12 (2) ◽  
pp. 1-17
Author(s):  
Qinqing Kang
Keyword(s):  
Large Scale ◽  
Attenuation Factor ◽  
Cluster Head ◽  
High Density ◽  
Wireless Sensor ◽  
Programming Algorithm ◽  
Localization Algorithm ◽  
Network Applications ◽  
Topology Information ◽  
Cluster Location

Node self-positioning is one of the supporting technologies for wireless sensor network applications. In this paper, a clustering localization algorithm is proposed for large-scale high-density wireless sensor networks. Firstly, the potential of the node is defined as the basis for the election of the cluster head. The distance between the nodes in the network is calculated indirectly by the relationship between the received signal strength and the communication radius. The topology information in each cluster is saved by the cluster head, and the linear programming method is used in the cluster head to implement the cluster internal relative positioning. Then, from the sink node, the inter-cluster location fusion is gradually implemented, and finally the absolute positioning of the whole network is realized. Compared with the centralized convex programming algorithm, the proposed algorithm has low computational complexity, small traffic, high positioning accuracy, and does not need to know the signal attenuation factor in the environment in advance, and there is anti-noise ability.

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