AIMC Topic: Microscopy, Atomic Force

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Wrinkle force microscopy: a machine learning based approach to predict cell mechanics from images.

Communications biology
Combining experiments with artificial intelligence algorithms, we propose a machine learning based approach called wrinkle force microscopy (WFM) to extract the cellular force distributions from the microscope images. The full process can be divided ...

Speeding up the Topography Imaging of Atomic Force Microscopy by Convolutional Neural Network.

Analytical chemistry
Atomic force microscopy (AFM) provides unprecedented insight into surface topography research with ultrahigh spatial resolution at the subnanometer level. However, a slow scanning rate has to be employed to ensure the image quality, which will largel...

Accelerating AFM Characterization via Deep-Learning-Based Image Super-Resolution.

Small (Weinheim an der Bergstrasse, Germany)
Atomic force microscopy (AFM) is one of the most popular imaging and characterizing methods applicable to a wide range of nanoscale material systems. However, high-resolution imaging using AFM generally suffers from a low scanning yield due to its me...

Traction force microscopy by deep learning.

Biophysical journal
Cells interact mechanically with their surroundings by exerting and sensing forces. Traction force microscopy (TFM), purported to map cell-generated forces or stresses, represents an important tool that has powered the rapid advances in mechanobiolog...

Fast Label-Free Nanoscale Composition Mapping of Eukaryotic Cells Via Scanning Dielectric Force Volume Microscopy and Machine Learning.

Small methods
Mapping the biochemical composition of eukaryotic cells without the use of exogenous labels is a long-sought objective in cell biology. Recently, it has been shown that composition maps on dry single bacterial cells with nanoscale spatial resolution ...

Learning-based event locating for single-molecule force spectroscopy.

Biochemical and biophysical research communications
Acquiring events massively from single-molecule force spectroscopy (SMFS) experiments, which is crucial for revealing important biophysical information, is usually not straightforward. A significant amount of human labor is usually required to identi...

Reconstruction of ARNT PAS-B Unfolding Pathways by Steered Molecular Dynamics and Artificial Neural Networks.

Journal of chemical theory and computation
Several experimental studies indicated that large conformational changes, including partial domain unfolding, have a role in the functional mechanisms of the basic helix loop helix Per/ARNT/SIM (bHLH-PAS) transcription factors. Recently, single-molec...

Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images.

The journal of physical chemistry. B
The maturation or activation status of dendritic cells (DCs) directly correlates with their behavior and immunofunction. A common means to determine the maturity of dendritic cells is from high-resolution images acquired via scanning electron microsc...

Numerical simulation of deformed red blood cell by utilizing neural network approach and finite element analysis.

Computer methods in biomechanics and biomedical engineering
In order to have research on the deformation characteristics and mechanical properties of human red blood cells (RBCs), finite element models of RBC optical tweezers stretching and atomic force microscope (AFM) indentation were established. Non-linea...

Image reconstruction for sub-sampled atomic force microscopy images using deep neural networks.

Micron (Oxford, England : 1993)
Undersampling is a simple but efficient way to increase the imaging rate of atomic force microscopy (AFM). One major challenge in this approach is that of accurate image reconstruction from a limited number of measurements. In this work, we present a...