scholarly journals Correction: A neural network-based algorithm for high-throughput characterisation of viscoelastic properties of flowing microcapsules

Soft Matter ◽  
2021 ◽  
Author(s):  
Tao Lin ◽  
Zhen Wang ◽  
Wen Wang ◽  
Yi Sui

Correction for ‘A neural network-based algorithm for high-throughput characterisation of viscoelastic properties of flowing microcapsules’ by Tao Lin et al., Soft Matter, 2021, DOI: 10.1039/d0sm02121k.

Soft Matter ◽  
2021 ◽  
Author(s):  
Tao Lin ◽  
Zhen Wang ◽  
Wen Wang ◽  
Yi Sui

We have developed a high-throughput method, by combining a hybrid neural network with a mechanistic capsule model, to predict membrane elasticity and viscosity of microcapsules from their dynamic deformation in a branched microchannel.


2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.


Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 264
Author(s):  
Kaisa Liimatainen ◽  
Riku Huttunen ◽  
Leena Latonen ◽  
Pekka Ruusuvuori

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Salman Sohrabi ◽  
Danielle E. Mor ◽  
Rachel Kaletsky ◽  
William Keyes ◽  
Coleen T. Murphy

AbstractWe recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson’s disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like ‘curling’ behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.


2019 ◽  
Vol 10 (36) ◽  
pp. 8374-8383 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Aditya Sonpal ◽  
Mojtaba Haghighatlari ◽  
Andrew J. Schultz ◽  
Johannes Hachmann

Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.


Lab on a Chip ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 3652-3663 ◽  
Author(s):  
Patricia M. Davidson ◽  
Gregory R. Fedorchak ◽  
Solenne Mondésert-Deveraux ◽  
Emily S. Bell ◽  
Philipp Isermann ◽  
...  

We report the development, validation, and application of an easy-to-use microfluidic micropipette aspiration device and automated image analysis platform that enables high-throughput measurements of the viscoelastic properties of cell nuclei.


BioTechniques ◽  
1996 ◽  
Vol 21 (6) ◽  
pp. 1110-1114 ◽  
Author(s):  
Guyang Matthew Huang ◽  
James Farkas ◽  
Leroy Hood

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