AIMC Topic: Microscopy, Atomic Force

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

Small (Weinheim an der Bergstrasse, Germany) Nov 27, 2021
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...
Deep Learning DNA Microscopy, Atomic Force
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Traction force microscopy by deep learning.

Biophysical journal Jun 30, 2021
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...
Computer Simulation Deep Learning Models, Theoretical Microscopy, Atomic Force Traction
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Fast Label-Free Nanoscale Composition Mapping of Eukaryotic Cells Via Scanning Dielectric Force Volume Microscopy and Machine Learning.

Small methods May 16, 2021
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 ...
Machine Learning Microscopy, Atomic Force Eukaryotic Cells
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Learning-based event locating for single-molecule force spectroscopy.

Biochemical and biophysical research communications Apr 8, 2021
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...
Deep Learning Microscopy, Atomic Force Optical Tweezers Single Molecule Imaging Automation
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Reconstruction of ARNT PAS-B Unfolding Pathways by Steered Molecular Dynamics and Artificial Neural Networks.

Journal of chemical theory and computation Mar 29, 2021
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...
Neural Networks, Computer Basic Helix-Loop-Helix Transcription Factors Molecular Dynamics Simulation Protein Unfolding Microscopy, Atomic Force Protein Conformation
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Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images.

The journal of physical chemistry. B Sep 22, 2020
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...
Machine Learning Dendritic Cells Microscopy, Atomic Force Microscopy, Electron, Scanning
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Numerical simulation of deformed red blood cell by utilizing neural network approach and finite element analysis.

Computer methods in biomechanics and biomedical engineering Aug 10, 2020
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...
Numerical Analysis, Computer-Assisted Erythrocyte Deformability Microscopy, Atomic Force Erythrocytes Elasticity Finite Element Analysis Stress, Mechanical Models, Biological Nonlinear Dynamics Algorithms Computer Simulation Humans Neural Networks, Computer
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Image reconstruction for sub-sampled atomic force microscopy images using deep neural networks.

Micron (Oxford, England : 1993) Dec 26, 2019
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...
Algorithms Neural Networks, Computer Microscopy, Atomic Force Image Processing, Computer-Assisted
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nanite: using machine learning to assess the quality of atomic force microscopy-enabled nano-indentation data.

BMC bioinformatics Sep 10, 2019
BACKGROUND: Atomic force microscopy (AFM) allows the mechanical characterization of single cells and live tissue by quantifying force-distance (FD) data in nano-indentation experiments. One of the main problems when dealing with biological tissue is ...
Machine Learning Animals Zebrafish Software Microscopy, Atomic Force Automation Nanotechnology Data Accuracy
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Engineering approaches for characterizing soft tissue mechanical properties: A review.

Clinical biomechanics (Bristol, Avon) Jul 16, 2019
From cancer diagnosis to detailed characterization of arterial wall biomechanics, the elastic property of tissues is widely studied as an early sign of disease onset. The fibrous structural features of tissues are a direct measure of its health and f...
Elasticity Imaging Techniques Stomach Bioengineering Tomography, Optical Coherence Cell Membrane Equipment Design Vagina Biomechanical Phenomena Area Under Curve Robotic Surgical Procedures Breast Male Diagnostic Imaging Female Microscopy, Atomic Force Prostate Fiber Optic Technology Brain Neoplasms Humans Neoplasms Elasticity Liver Machine Learning
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