scholarly journals Super-resolution imaging reveals distinct chromatin folding for different epigenetic states

Nature ◽  
2016 ◽  
Vol 529 (7586) ◽  
pp. 418-422 ◽  
Author(s):  
Alistair N. Boettiger ◽  
Bogdan Bintu ◽  
Jeffrey R. Moffitt ◽  
Siyuan Wang ◽  
Brian J. Beliveau ◽  
...  
Keyword(s):  
Super Resolution ◽  
Chromatin Folding ◽  
Resolution Imaging

scholarly journals Super-resolution imaging reveals the evolution of higher-order chromatin folding in early carcinogenesis

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jianquan Xu ◽  
Hongqiang Ma ◽  
Hongbin Ma ◽  
Wei Jiang ◽  
Christopher A. Mela ◽  
...  
Keyword(s):  
Super Resolution ◽  
Higher Order ◽  
Chromatin Folding ◽  
Resolution Imaging ◽  
Early Carcinogenesis

scholarly journals Characterizing chromatin folding coordinate and landscape with deep learning

2019 ◽  
Author(s):  
Wen Jun Xie ◽  
Yifeng Qi ◽  
Bin Zhang
Keyword(s):  
Deep Learning ◽  
Driving Forces ◽  
Super Resolution ◽  
Variational Autoencoder ◽  
Spatial Environment ◽  
Substantial Progress ◽  
Random Structures ◽  
Chromatin Folding ◽  
Resolution Imaging ◽  
Folded Structures

Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less understood. We applied a deep-learning approach, variational autoencoder (VAE), to analyze the fluctuation and heterogeneity of chromatin structures revealed by single-cell super-resolution imaging and to identify a reaction coordinate for chromatin folding. This coordinate monitors the progression of topologically associating domain (TAD) formation and connects the seemingly random structures observed in individual cohesin-depleted cells as intermediate states along the folding pathway. Analysis of the folding landscape derived from VAE suggests that well-folded structures similar to those found in wild-type cells remain energetically favorable in cohesin-depleted cells. The interaction energies, however, are not strong enough to overcome the entropic penalty, leading to the formation of only partially folded structures and the disappearance of TADs from contact maps upon averaging. Implications of these results for the molecular driving forces of chromatin folding are discussed.

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

scholarly journals 3D super-resolved imaging in live cells using sub-diffractive plasmonic localization of hybrid nanopillar arrays

Nanophotonics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 2847-2859
Author(s):  
Soojung Kim ◽  
Hyerin Song ◽  
Heesang Ahn ◽  
Seung Won Jun ◽  
Seungchul Kim ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Imaging Techniques ◽  
Optical Loss ◽  
Super Resolution ◽  
Living Cells ◽  
Live Cells ◽  
Complex Biological System ◽  
Observation Volume ◽  
Resolution Imaging ◽  
Nanopillar Arrays

AbstractAnalysing dynamics of a single biomolecule using high-resolution imaging techniques has been had significant attentions to understand complex biological system. Among the many approaches, vertical nanopillar arrays in contact with the inside of cells have been reported as a one of useful imaging applications since an observation volume can be confined down to few-tens nanometre theoretically. However, the nanopillars experimentally are not able to obtain super-resolution imaging because their evanescent waves generate a high optical loss and a low signal-to-noise ratio. Also, conventional nanopillars have a limitation to yield 3D information because they do not concern field localization in z-axis. Here, we developed novel hybrid nanopillar arrays (HNPs) that consist of SiO2 nanopillars terminated with gold nanodisks, allowing extreme light localization. The electromagnetic field profiles of HNPs are obtained through simulations and imaging resolution of cell membrane and biomolecules in living cells are tested using one-photon and 3D multiphoton fluorescence microscopy, respectively. Consequently, HNPs present approximately 25 times enhanced intensity compared to controls and obtained an axial and lateral resolution of 110 and 210 nm of the intensities of fluorophores conjugated with biomolecules transported in living cells. These structures can be a great platform to analyse complex intracellular environment.

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