scholarly journals Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-learning Transformation of Phase-contrast into Fluorescence Microscopy Images

2021 ◽  
Author(s):  
Jiangguo Zhang ◽  
Jessica A. Comstock ◽  
Christopher R. Cotter ◽  
Patrick A. Murphy ◽  
Weili Nie ◽  
...  
Keyword(s):  
Pattern Formation ◽  
Fluorescence Microscopy ◽  
Cell Density ◽  
Phase Contrast ◽  
Fluorescent Proteins ◽  
Myxococcus Xanthus ◽  
Accurate Estimation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Microscopy Images

Myxococcus xanthus bacteria are a model system for understanding pattern formation and collective cell behaviors. When starving, cells aggregate into fruiting bodies to form metabolically inert spores. During predation, cells self-organize into traveling cell-density waves termed ripples. Both phase-contrast and fluorescence microscopy are used to observe these patterns but each has its limitations. Phase-contrast images have higher contrast, but the resulting image intensities lose their correlation with cell density. The intensities of fluorescence microscopy images, on the other hand, are well correlated with cell density, enabling better segmentation of aggregates and better visualization of streaming patterns in between aggregates. However, fluorescence microscopy requires the engineering of cells to express fluorescent proteins and can be phototoxic to the cells. To combine the advantages of both imaging methodologies, we develop a generative adversarial network that converts phase-contrast into fluorescent images. By including an additional histogram-equalized output to the state-of-art pix2pixHD algorithm, our model generates accurate images of aggregates and streams, enabling the estimation of aggregate positions and sizes, but with small shifts of their boundaries. Further training on ripple patterns enables accurate estimation of the rippling wavelength. Our methods are thus applicable for many other phenotypic behaviors and pattern formation studies.

scholarly journals Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-Learning Transformation of Phase-Contrast into Fluorescence Microscopy Images

2021 ◽  
Vol 9 (9) ◽  
pp. 1954
Author(s):  
Jiangguo Zhang ◽  
Jessica A. Comstock ◽  
Christopher R. Cotter ◽  
Patrick A. Murphy ◽  
Weili Nie ◽  
...  
Keyword(s):  
Pattern Formation ◽  
Fluorescence Microscopy ◽  
Cell Density ◽  
Phase Contrast ◽  
Fluorescent Proteins ◽  
Myxococcus Xanthus ◽  
Accurate Estimation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Microscopy Images

Myxococcus xanthus bacteria are a model system for understanding pattern formation and collective cell behaviors. When starving, cells aggregate into fruiting bodies to form metabolically inert spores. During predation, cells self-organize into traveling cell-density waves termed ripples. Both phase-contrast and fluorescence microscopy are used to observe these patterns but each has its limitations. Phase-contrast images have higher contrast, but the resulting image intensities lose their correlation with cell density. The intensities of fluorescence microscopy images, on the other hand, are well-correlated with cell density, enabling better segmentation of aggregates and better visualization of streaming patterns in between aggregates; however, fluorescence microscopy requires the engineering of cells to express fluorescent proteins and can be phototoxic to cells. To combine the advantages of both imaging methodologies, we develop a generative adversarial network that converts phase-contrast into synthesized fluorescent images. By including an additional histogram-equalized output to the state-of-the-art pix2pixHD algorithm, our model generates accurate images of aggregates and streams, enabling the estimation of aggregate positions and sizes, but with small shifts of their boundaries. Further training on ripple patterns enables accurate estimation of the rippling wavelength. Our methods are thus applicable for many other phenotypic behaviors and pattern formation studies.

scholarly journals Nuclear incorporation of iron during the eukaryotic cell cycle

2016 ◽  
Vol 23 (6) ◽  
pp. 1490-1497 ◽  
Author(s):  
Ian Robinson ◽  
Yang Yang ◽  
Fucai Zhang ◽  
Christophe Lynch ◽  
Mohammed Yusuf ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Fluorescence Microscopy ◽  
Phase Contrast ◽  
Eukaryotic Cell ◽  
Inhomogeneous Distribution ◽  
Cell Nuclei ◽  
X Ray ◽  
Microscopy Images ◽  
Different Levels

Scanning X-ray fluorescence microscopy has been used to probe the distribution of S, P and Fe within cell nuclei. Nuclei, which may have originated at different phases of the cell cycle, are found to show very different levels of Fe present with a strongly inhomogeneous distribution. P and S signals, presumably from DNA and associated nucleosomes, are high and relatively uniform across all the nuclei; these agree with X-ray phase contrast projection microscopy images of the same samples. Possible reasons for the Fe incorporation are discussed.

Virtual sensor for probabilistic estimation of the evaporation in cooling towers

2021 ◽  
pp. 1-13
Author(s):  
Serafín Alonso ◽  
Antonio Morán ◽  
Daniel Pérez ◽  
Miguel A. Prada ◽  
Juan J. Fuertes ◽  
...  
Keyword(s):  
Management Strategies ◽  
Real Data ◽  
Accurate Estimation ◽  
Cooling Towers ◽  
Generative Adversarial Network ◽  
Virtual Sensor ◽  
Efficient Operation ◽  
Probabilistic Estimation ◽  
Adversarial Network ◽  
Generative Approach

Global natural resources are affected by several causes such as climate change effects or unsustainable management strategies. Indeed, the use of water has been intensified in urban buildings because of the proliferation of HVAC (Heating, Ventilating and Air Conditioning) systems, for instance cooling towers, where an abundant amount of water is lost during the evaporation process. The measurement of the evaporation is challenging, so a virtual sensor could be used to tackle it, allowing to monitor and manage the water consumption in different scenarios and helping to plan efficient operation strategies which reduce the use of fresh water. In this paper, a deep generative approach is proposed for developing a virtual sensor for probabilistic estimation of the evaporation in cooling towers, given the surrounding conditions. It is based on a conditioned generative adversarial network (cGAN), whose generator includes a recurrent layer (GRU) that models the temporal information by learning from previous states and a densely connected layer that models the fluctuations of the conditions. The proposed deep generative approach is not only able to yield the estimated evaporation value but it also produces a whole probability distribution, considering any operating scenario, so it is possible to know the confidence interval in which the estimation is likely found. This deep generative approach is assessed and compared with other probabilistic state-of-the-art methods according to several metrics (CRPS, MAPE and RMSE) and using real data from a cooling tower located at a hospital building. The results obtained show that, to the best of our knowledge, our proposal is a noteworthy method to develop a virtual sensor, taking as input the current and last samples, since it provides an accurate estimation of the evaporation with wide enough confidence intervals, contemplating potential fluctuations of the conditions.

scholarly journals Deep learning achieves super-resolution in fluorescence microscopy

2018 ◽  
Author(s):  
Hongda Wang ◽  
Yair Rivenson ◽  
Yiyin Jin ◽  
Zhensong Wei ◽  
Ronald Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Numerical Models ◽  
Numerical Aperture ◽  
Super Resolution ◽  
Image Resolution ◽  
Stimulated Emission ◽  
Wide Field ◽  
Generative Adversarial Network ◽  
Adversarial Network

AbtsractWe present a deep learning-based method for achieving super-resolution in fluorescence microscopy. This data-driven approach does not require any numerical models of the imaging process or the estimation of a point spread function, and is solely based on training a generative adversarial network, which statistically learns to transform low resolution input images into super-resolved ones. Using this method, we super-resolve wide-field images acquired with low numerical aperture objective lenses, matching the resolution that is acquired using high numerical aperture objectives. We also demonstrate that diffraction-limited confocal microscopy images can be transformed by the same framework into super-resolved fluorescence images, matching the image resolution acquired with a stimulated emission depletion (STED) microscope. The deep network rapidly outputs these super-resolution images, without any iterations or parameter search, and even works for types of samples that it was not trained for.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Role of General Adversarial Networks in Mammogram Analysis: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 863-877
Author(s):  
Annapoorani Gopal ◽  
Lathaselvi Gandhimaruthian ◽  
Javid Ali
Keyword(s):  
Breast Tumor ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Technology ◽  
Breast Cancers ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

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