scholarly journals Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Romain F Laine ◽  
Gemma Goodfellow ◽  
Laurence J Young ◽  
Jon Travers ◽  
Danielle Carroll ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Vaccine Development ◽  
Functional Performance ◽  
Rapid Assessment ◽  
Super Resolution ◽  
Machine Learning Algorithms ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
High Throughput Analysis

Optical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line.

scholarly journals Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure

2018 ◽  
Author(s):  
Romain F. Laine ◽  
Gemma Goodfellow ◽  
Laurence J. Young ◽  
Jon Travers ◽  
Danielle Carroll ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Vaccine Development ◽  
Functional Performance ◽  
Rapid Assessment ◽  
Super Resolution ◽  
Machine Learning Algorithms ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
High Throughput Analysis

AbstractOptical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line.

scholarly journals Accelerating organic solar cell material's discovery: high-throughput screening and big data

2021 ◽  
Author(s):  
Xabier Rodríguez-Martínez ◽  
Enrique Pascual-San-José ◽  
Mariano Campoy-Quiles
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Organic Solar Cell ◽  
State Of The Art ◽  
Review Article ◽  
Machine Learning Algorithms ◽  
Device Optimization

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

scholarly journals Two-dimensional TIRF-SIM–traction force microscopy (2D TIRF-SIM-TFM)

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Liliana Barbieri ◽  
Huw Colin-York ◽  
Kseniya Korobchevskaya ◽  
Di Li ◽  
Deanna L. Wolfson ◽  
...  
Keyword(s):  
Resolution Enhancement ◽  
Traction Force ◽  
Super Resolution ◽  
Traction Force Microscopy ◽  
Two Dimensional ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Force Microscopy ◽  
Health And Disease ◽  
Spatio Temporal

AbstractQuantifying small, rapidly evolving forces generated by cells is a major challenge for the understanding of biomechanics and mechanobiology in health and disease. Traction force microscopy remains one of the most broadly applied force probing technologies but typically restricts itself to slow events over seconds and micron-scale displacements. Here, we improve >2-fold spatially and >10-fold temporally the resolution of planar cellular force probing compared to its related conventional modalities by combining fast two-dimensional total internal reflection fluorescence super-resolution structured illumination microscopy and traction force microscopy. This live-cell 2D TIRF-SIM-TFM methodology offers a combination of spatio-temporal resolution enhancement relevant to forces on the nano- and sub-second scales, opening up new aspects of mechanobiology to analysis.

scholarly journals Double moiré localized plasmon structured illumination microscopy

Nanophotonics ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ruslan Röhrich ◽  
A. Femius Koenderink
Keyword(s):  
Near Field ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Experimental Realization ◽  
Spatial Frequencies ◽  
Wide Range ◽  
Localized Plasmon ◽  
Plasmonic Grating

AbstractStructured illumination microscopy (SIM) is a well-established fluorescence imaging technique, which can increase spatial resolution by up to a factor of two. This article reports on a new way to extend the capabilities of structured illumination microscopy, by combining ideas from the fields of illumination engineering and nanophotonics. In this technique, plasmonic arrays of hexagonal symmetry are illuminated by two obliquely incident beams originating from a single laser. The resulting interference between the light grating and plasmonic grating creates a wide range of spatial frequencies above the microscope passband, while still preserving the spatial frequencies of regular SIM. To systematically investigate this technique and to contrast it with regular SIM and localized plasmon SIM, we implement a rigorous simulation procedure, which simulates the near-field illumination of the plasmonic grating and uses it in the subsequent forward imaging model. The inverse problem, of obtaining a super-resolution (SR) image from multiple low-resolution images, is solved using a numerical reconstruction algorithm while the obtained resolution is quantitatively assessed. The results point at the possibility of resolution enhancements beyond regular SIM, which rapidly vanishes with the height above the grating. In an initial experimental realization, the existence of the expected spatial frequencies is shown and the performance of compatible reconstruction approaches is compared. Finally, we discuss the obstacles of experimental implementations that would need to be overcome for artifact-free SR imaging.

scholarly journals Rapid bacteria identification using structured illumination microscopy and machine learning

2017 ◽  
Vol 11 (01) ◽  
pp. 1850007 ◽  
Author(s):  
Yingchuan He ◽  
Weize Xu ◽  
Yao Zhi ◽  
Rohit Tyagi ◽  
Zhe Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Microscopy ◽  
Pathogenic Bacteria ◽  
Morphological Features ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Standard Pattern ◽  
Bacteria Identification ◽  
Detection And Identification ◽  
Combined Use

Traditionally, optical microscopy is used to visualize the morphological features of pathogenic bacteria, of which the features are further used for the detection and identification of the bacteria. However, due to the resolution limitation of conventional optical microscopy as well as the lack of standard pattern library for bacteria identification, the effectiveness of this optical microscopy-based method is limited. Here, we reported a pilot study on a combined use of Structured Illumination Microscopy (SIM) with machine learning for rapid bacteria identification. After applying machine learning to the SIM image datasets from three model bacteria (including Escherichia coli, Mycobacterium smegmatis, and Pseudomonas aeruginosa), we obtained a classification accuracy of up to 98%. This study points out a promising possibility for rapid bacterial identification by morphological features.

scholarly journals High-fidelity structured illumination microscopy by point-spread-function engineering

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Gang Wen ◽  
Simin Li ◽  
Linbo Wang ◽  
Xiaohu Chen ◽  
Zhenglong Sun ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
High Fidelity ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Imaging Tool ◽  
Point Spread ◽  
Spread Function ◽  
Point Spread Function Engineering

AbstractStructured illumination microscopy (SIM) has become a widely used tool for insight into biomedical challenges due to its rapid, long-term, and super-resolution (SR) imaging. However, artifacts that often appear in SIM images have long brought into question its fidelity, and might cause misinterpretation of biological structures. We present HiFi-SIM, a high-fidelity SIM reconstruction algorithm, by engineering the effective point spread function (PSF) into an ideal form. HiFi-SIM can effectively reduce commonly seen artifacts without loss of fine structures and improve the axial sectioning for samples with strong background. In particular, HiFi-SIM is not sensitive to the commonly used PSF and reconstruction parameters; hence, it lowers the requirements for dedicated PSF calibration and complicated parameter adjustment, thus promoting SIM as a daily imaging tool.

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