Anomalous Diffusion in Thermoresponsive Polymer–Clay Composite Hydrogels Probed by Wide-Field Fluorescence Microscopy

Langmuir ◽  
2014 ◽  
Vol 30 (46) ◽  
pp. 14056-14061 ◽  
Author(s):  
Beate Stempfle ◽  
Anna Große ◽  
Bernhard Ferse ◽  
Karl-Friedrich Arndt ◽  
Dominik Wöll
2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Luzhe Huang ◽  
Hanlong Chen ◽  
Yilin Luo ◽  
Yair Rivenson ◽  
Aydogan Ozcan

AbstractVolumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume. Through a recurrent convolutional neural network, which we term as Recurrent-MZ, 2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field. Using experiments on C. elegans and nanobead samples, Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens, also providing a 30-fold reduction in the number of axial scans required to image the same sample volume. We further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions, including e.g., different sequences of input images, covering various axial permutations and unknown axial positioning errors. We also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input, matching confocal microscopy images of the same sample volume. Recurrent-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework, overcoming the limitations of current 3D scanning microscopy tools.


2021 ◽  
Vol 11 (6) ◽  
pp. 2773
Author(s):  
Hiroaki Yokota ◽  
Atsuhito Fukasawa ◽  
Minako Hirano ◽  
Toru Ide

Over the years, fluorescence microscopy has evolved and has become a necessary element of life science studies. Microscopy has elucidated biological processes in live cells and organisms, and also enabled tracking of biomolecules in real time. Development of highly sensitive photodetectors and light sources, in addition to the evolution of various illumination methods and fluorophores, has helped microscopy acquire single-molecule fluorescence sensitivity, enabling single-molecule fluorescence imaging and detection. Low-light photodetectors used in microscopy are classified into two categories: point photodetectors and wide-field photodetectors. Although point photodetectors, notably photomultiplier tubes (PMTs), have been commonly used in laser scanning microscopy (LSM) with a confocal illumination setup, wide-field photodetectors, such as electron-multiplying charge-coupled devices (EMCCDs) and scientific complementary metal-oxide-semiconductor (sCMOS) cameras have been used in fluorescence imaging. This review focuses on the former low-light point photodetectors and presents their fluorescence microscopy applications and recent progress. These photodetectors include conventional PMTs, single photon avalanche diodes (SPADs), hybrid photodetectors (HPDs), in addition to newly emerging photodetectors, such as silicon photomultipliers (SiPMs) (also known as multi-pixel photon counters (MPPCs)) and superconducting nanowire single photon detectors (SSPDs). In particular, this review shows distinctive features of HPD and application of HPD to wide-field single-molecule fluorescence detection.


ACS Sensors ◽  
2018 ◽  
Vol 3 (12) ◽  
pp. 2644-2650 ◽  
Author(s):  
Xiaojun Liu ◽  
Conghui Huang ◽  
Chenghua Zong ◽  
Aiye Liang ◽  
Zhangjian Wu ◽  
...  

2015 ◽  
Vol 87 (9) ◽  
pp. 4675-4682 ◽  
Author(s):  
Cyril Ruckebusch ◽  
Romain Bernex ◽  
Franco Allegrini ◽  
Michel Sliwa ◽  
Johan Hofkens ◽  
...  

2013 ◽  
Vol 85 (4) ◽  
pp. 2356-2360 ◽  
Author(s):  
Chao Han ◽  
Shuo Pang ◽  
Danielle V. Bower ◽  
Patrick Yiu ◽  
Changhuei Yang

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