AIMC Topic: Rats
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Machine-learning assisted confocal imaging of intracellular sites of triglycerides and cholesteryl esters formation and storage.
Analytica chimica acta
May 3, 2020
All living systems are maintained by a constant flux of metabolic energy and, among the different reactions, the process of lipids storage and lipolysis is of fundamental importance. Current research has focused on the investigation of lipid droplets...
On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective.
Neural networks : the official journal of the International Neural Network Society
Apr 18, 2020
The reconsolidation and extinction of aversive memories and their boundary conditions have been extensively studied. Knowing their network mechanisms may lead to the development of better strategies for the treatment of fear and anxiety-related disor...
From spatial navigation via visual construction to episodic memory and imagination.
Biological cybernetics
Apr 13, 2020
This hybrid of review and personal essay argues that models of visual construction are essential to extend spatial navigation models to models that link episodic memory and imagination. The starting point is the TAM-WG model, combining the Taxon Affo...
In Silico Prediction of Metabolic Epoxidation for Drug-like Molecules via Machine Learning Methods.
Molecular informatics
Mar 31, 2020
Epoxidation is one of the reactions in drug metabolism. Since epoxide metabolites would bind with proteins or DNA covalently, drugs should avoid epoxidation metabolism in the body. Due to the instability of epoxide, it is difficult to determine epoxi...
Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning.
Molecules (Basel, Switzerland)
Mar 13, 2020
The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals c...
Deep learning-based target metabolite isolation and big data-driven measurement uncertainty estimation in proton magnetic resonance spectroscopy of the brain.
Magnetic resonance in medicine
Mar 5, 2020
PURPOSE: The aim of this study was to develop a method for metabolite quantification with simultaneous measurement uncertainty estimation in deep learning-based proton magnetic resonance spectroscopy ( H-MRS).
A Deep Learning Approach for Automated Sleep-Wake Scoring in Pre-Clinical Animal Models.
Journal of neuroscience methods
Mar 2, 2020
BACKGROUND: Experimental investigation of sleep-wake dynamics in animals is an important part of pharmaceutical development. Typically, it involves recording of electroencephalogram, electromyogram, locomotor activity, and electrooculogram. Visual id...
Analysis of neurite length of hippocampal neurons cultured into 3D artificial network patterned microfluidic chips.
The International journal of neuroscience
Feb 28, 2020
The study aims to lay a foundational probe for the thorough application microfluidic chips in brain function research with microfluidic chips. Neuron slide culture is a common culture method , and the microfluidic chip with the artificial network pa...
Bioinspired Soft Robot with Incorporated Microelectrodes.
Journal of visualized experiments : JoVE
Feb 28, 2020
Bioinspired soft robotic systems that mimic living organisms using engineered muscle tissue and biomaterials are revolutionizing the current biorobotics paradigm, especially in biomedical research. Recreating artificial life-like actuation dynamics i...
Real-time sensory-motor integration of hippocampal place cell replay and prefrontal sequence learning in simulated and physical rat robots for novel path optimization.
Biological cybernetics
Feb 24, 2020
An open problem in the cognitive dimensions of navigation concerns how previous exploratory experience is reorganized in order to allow the creation of novel efficient navigation trajectories. This behavior is revealed in the "traveling salesrat prob...
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