AIMC Topic: Mice

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Fast fit-free analysis of fluorescence lifetime imaging via deep learning.

Proceedings of the National Academy of Sciences of the United States of America
Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI...

Automated CT-derived skeletal muscle mass determination in lower hind limbs of mice using a 3D U-Net deep learning network.

Journal of applied physiology (Bethesda, Md. : 1985)
The loss of skeletal muscle mass is recognized as a complication of several chronic diseases and is associated with increased mortality and a decreased quality of life. Relevant and reliable animal models in which muscle wasting can be monitored noni...

MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks.

Scientific reports
Automated sleep stage scoring for mice is in high demand for sleep research, since manual scoring requires considerable human expertise and efforts. The existing automated scoring methods do not provide the scoring accuracy required for practical use...

A deep learning framework to predict binding preference of RNA constituents on protein surface.

Nature communications
Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes...

Comprehensive anatomic ontologies for lung development: A comparison of alveolar formation and maturation within mouse and human lung.

Journal of biomedical semantics
BACKGROUND: Although the mouse is widely used to model human lung development, function, and disease, our understanding of the molecular mechanisms involved in alveolarization of the peripheral lung is incomplete. Recently, the Molecular Atlas of Lun...

Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics.

Nature methods
We engineered light-gated channelrhodopsins (ChRs) whose current strength and light sensitivity enable minimally invasive neuronal circuit interrogation. Current ChR tools applied to the mammalian brain require intracranial surgery for transgene deli...

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images.

IEEE transactions on medical imaging
We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences-motifs that are visually similar across different acquisitions-to infer changes on the ...

Pathway-Based Single-Cell RNA-Seq Classification, Clustering, and Construction of Gene-Gene Interactions Networks Using Random Forests.

IEEE journal of biomedical and health informatics
Single-cell RNA-Sequencing (scRNA-Seq), an advanced sequencing technique, enables biomedical researchers to characterize cell-specific gene expression profiles. Although studies have adapted machine learning algorithms to cluster different cell popul...

Task-Dependent Changes in the Large-Scale Dynamics and Necessity of Cortical Regions.

Neuron
Neural activity throughout the cortex is correlated with perceptual decisions, but inactivation studies suggest that only a small number of areas are necessary for these behaviors. Here we show that the number of required cortical areas and their dyn...

An in vitro assay and artificial intelligence approach to determine rate constants of nanomaterial-cell interactions.

Scientific reports
In vitro assays and simulation technologies are powerful methodologies that can inform scientists of nanomaterial (NM) distribution and fate in humans or pre-clinical species. For small molecules, less animal data is often needed because there are a ...