AIMC Topic: Mice

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An open-source tool for analysis and automatic identification of dendritic spines using machine learning.

PloS one
Synaptic plasticity, the cellular basis for learning and memory, is mediated by a complex biochemical network of signaling proteins. These proteins are compartmentalized in dendritic spines, the tiny, bulbous, post-synaptic structures found on neuron...

Catechin rich butanol fraction extracted from Acacia catechu L. (a thirst quencher) exhibits immunostimulatory potential.

Journal of food and drug analysis
Acacia catechu L., (Fabaceae) named as "catechu" is a plant, the decoction of heartwood of which is daily consumed as thirst quencher by a good percentage of the population in South India. The plant is mainly distributed in India and other Asian coun...

Multi-Factored Gene-Gene Proximity Measures Exploiting Biological Knowledge Extracted from Gene Ontology: Application in Gene Clustering.

IEEE/ACM transactions on computational biology and bioinformatics
To describe the cellular functions of proteins and genes, a potential dynamic vocabulary is Gene Ontology (GO), which comprises of three sub-ontologies namely, Biological-process, Cellular-component, and Molecular-function. It has several application...

Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis.

Neuron
Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it rema...

Expanding the horizons of microRNA bioinformatics.

RNA (New York, N.Y.)
MicroRNA regulation of key biological and developmental pathways is a rapidly expanding area of research, accompanied by vast amounts of experimental data. This data, however, is not widely available in bioinformatic resources, making it difficult fo...

Large Scale Image Segmentation with Structured Loss Based Deep Learning for Connectome Reconstruction.

IEEE transactions on pattern analysis and machine intelligence
We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy (EM) in accuracy and scalability. Our method consists of a 3D U-Net, t...

The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks.

PloS one
Local perturbations within complex dynamical systems can trigger cascade-like events that spread across significant portions of the system. Cascades of this type have been observed across a broad range of scales in the brain. Studies of these cascade...

Cell dynamic morphology classification using deep convolutional neural networks.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Cell morphology is often used as a proxy measurement of cell status to understand cell physiology. Hence, interpretation of cell dynamic morphology is a meaningful task in biomedical research. Inspired by the recent success of deep learning, we here ...

Predicting lysine-malonylation sites of proteins using sequence and predicted structural features.

Journal of computational chemistry
Malonylation is a recently discovered post-translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT-Mal, is developed to predict malonylat...

Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.

Stem cell reports
Deep learning technology is rapidly advancing and is now used to solve complex problems. Here, we used deep learning in convolutional neural networks to establish an automated method to identify endothelial cells derived from induced pluripotent stem...