AIMC Topic: Microscopy, Fluorescence

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Evaluating safe infrared neural stimulation parameters: Calcium dynamics and excitotoxicity thresholds in dorsal root ganglia neurons.

Journal of neuroscience methods
BACKGROUND: As a promising neural stimulation technique, infrared neural stimulation (INS) has recently gained significant attention due to its ability to stimulate neuronal activities without needing exogenous agents. NIR light is absorbed by water ...

A Meta-Learning Approach for Multicenter and Small-Data Single-Cell Image Analysis.

Analytical chemistry
The application of algorithm-based single-cell imaging techniques can visualize and analyze cellular heterogeneity. However, algorithm-based single-cell imaging techniques are severely limited by the high workload required to label single-cell images...

Deep Learning for Fluorescence Lifetime Predictions Enables High-Throughput In Vivo Imaging.

Journal of the American Chemical Society
Fluorescence lifetime imaging microscopy (FLIM) is a powerful optical tool widely used in biomedical research to study changes in a sample's microenvironment. However, data collection and interpretation are often challenging, and traditional methods ...

The Application of Anisotropically Collapsing Gels, Deep Learning, and Optical Microscopy for Chemical Characterization of Nanoparticles and Nanoplastics.

Langmuir : the ACS journal of surfaces and colloids
The surface chemistry of nanomaterials, particularly the density of functional groups, governs their behavior in applications such as bioanalysis, bioimaging, and environmental impact studies. Here, we report a precise method to quantify carboxyl gro...

Multiplexing and Sensing with Fluorescence Lifetime Imaging Microscopy Empowered by Phasor U-Net.

Analytical chemistry
Fluorescence lifetime imaging microscopy (FLIM) has been widely used as an essential multiplexing and sensing tool in frontier fields such as materials science and life sciences. However, the accuracy of lifetime estimation is compromised by limited ...

Local mean suppression filter for effective background identification in fluorescence images.

Computers in biology and medicine
We present an easy-to-use, nonlinear filter for effective background identification in fluorescence microscopy images with dense and low-contrast foreground. The pixel-wise filtering is based on comparison of the pixel intensity with the mean intensi...

Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification.

Nature communications
The objective of optical super-resolution imaging is to acquire reliable sub-diffraction information on bioprocesses to facilitate scientific discovery. Structured illumination microscopy (SIM) is acknowledged as the optimal modality for live-cell su...

Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy.

Proceedings of the National Academy of Sciences of the United States of America
Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise ...

Deep Learning-Based Image Restoration and Super-Resolution for Fluorescence Microscopy: Overview and Resources.

Methods in molecular biology (Clifton, N.J.)
Fluorescence microscopy is a key method for the visualization of cellular, subcellular, and molecular live-cell dynamics, enabling access to novel insights into mechanisms of health and disease. However, effects like phototoxicity, the fugitive natur...

A framework of multi-view machine learning for biological spectral unmixing of fluorophores with overlapping excitation and emission spectra.

Briefings in bioinformatics
The accuracy of assigning fluorophore identity and abundance, known as spectral unmixing, in biological fluorescence microscopy images remains a significant challenge due to the substantial overlap in emission spectra among fluorophores. In tradition...